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Domain Vocabulary for Business Intelligence

Project Acronym UNDERSTANDER

Document-Id D.1

File name

Version Final document

Date Start: 10 September 2013

End: 31 October 2013

Author(s) Violeta Damjanovic (SRFG)

Wernher Behrendt (SRFG)

QA Process Verteiler: Prüfung durch: Genehmigung durch:

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Table of Contents

1. Introduction 1.1. Motivation 1.2. Scope 1.3. Structure of the Document

2. Related Work 2.1. Related Work in Business Intelligence

2.1.1. Decision Support Systems 2.1.2. Semantic Web Technologies 2.1.3. BI Approaches, Tools and Models

2.2. Related Work in Competitive Intelligence 3. Domain Vocabulary for Business Intelligence

3.1. UNDERSTANDER Technology Pillars 3.1.1. Business Intelligence Model (BIM) 3.1.2. Taxonomy of Competitive Intelligence 3.1.3. Conceptual Dependency Theory

3.2. UNDERSTANDER Domain Vocabulary for BI 3.2.1. Implementing BIM Concepts and Relationships in SWI-Prolog 3.2.2. Implementing CI Concepts and Relationships in SWI-Prolog 3.2.3. Implementing CD Concepts in SWI-Prolog

4. Conclusion Appendix 1 - Mind Mapping’s Taxonomy of CI Appendix 2 - Questionnaire on BI/CI Industry Needs References

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1. Introduction The table below summarizes the main project goals, description of the content, methods and milestones related to UNDERSTANDER Work Package 1 (WP1).

Goals The main goal of this report is to develop a controlled vocabulary and taxonomy of terms for Business Intelligence (BI) and Competitive Intelligence (CI). This will be the basis for the next task: building BI scripts that the agent has to recognise when encountering a relevant web resource.

Description of the content

Terminology collection: ● Nouns such as firm, competitor, market, …. ● Verbs such as: acquiring, selling, licensing, manufacturing, … ● Phrases are collected because they serve as templates for semantic

patterns, e.g.: “firm X is a competitor of firm Y in market Z”. ● Some phrases refer to process knowledge, e.g.: “technology X is used

in manufacturing process Y to achieve result Z”.

Method ● Desktop research on the basis of actual BI reports ● Interviews with managers in industry (see Appendix 2 for the

questionnaire) ● First-pass matching of phrases, terms and process knowledge with

Conceptual Dependency (CD) theory.

Project milestones

● Domain vocabulary for BI (full taxonomy) ● Overview paper (to be submitted at a relevant conference or

workshop)

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1.1. Motivation This document explores knowledge perspectives of Business Intelligence (BI) and Competitive Intelligence (CI) as information means capable of supporting the future design, implementation and applications of BI 3.0. We consider BI as a knowledge phenomenon, which is focused on what needs to be known in order to: (a) stimulate new knowledge on various BI-related topics, and (b) perform the knowledge in a way that brings more understanding about its context. In other words, we define BI as a knowledge phenomenon that stimulates overall process knowledge performing a manufacturing process. UNDERSTANDER addresses the core problem of any form of BI, irrespective of whether it is at web-scale or just a local solution for a corporate document management system. The core problem is that currently available technology relies much on statistics, may use a certain amount of (database) structure, but it rarely uses actual “knowledge”. In other words: current systems are not relying much on explicit knowledge models and are not using any significant degree of heuristic rules at any level that is close to human abstraction levels. However, as the term “Business (+) Intelligence” implies, meaningful interpretation of any new information requires pre-existing knowledge of the business for which relevant information is being gathered. It also requires some form of intelligent behaviour for any “intelligent” acting based on the new information. There are several fundamental methodological choices addressing BI, such as:

(1) Statistical approaches over very large data sets and query sets: The “Google” or “Amazon” approach typically works on sophisticated indexes derived from the queries;

(2) Neural network approaches with machine learning algorithms, in which the neural processor learns certain classification functions, either through supervised or unsupervised learning;

(3) The modelling-in-the-large approach as has been used in the long-term Cyc project, with many models and sub-models (micro-theories) interlinked with one another;

(4) The academically-led “Linked Data” and Semantic Web approach, in which small schemas are used to encode information via RDF (Resource Definition Framework) and queries on top of the RDF-graph yield sub-graphs as result sets;

(5) Industry-led approaches under different usage paradigms: for example, the schema.org approach as offered by Google, Yahoo and Microsoft; the knowledge based search approach as tried by the Wolfram Alpha search engine, or the mimicking of “general knowledge” as recently demonstrated by the IBM Watson project, which is a popular US quiz-show based on a hybrid machinery.

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Taking the statistical approaches (as explained above, under (1)) presupposes access to large data and query sets. It is only very few large web service providers, such as Google, Amazon, ebay, facebook, Yahoo!, who posses the necessary data repositories and computational resources, and who can successfully apply statistical approaches for gathering intelligence about (non) web-based content. In addition, in most decision support systems it is important to find data elements relevant to a business need (or situation), rather than only documents. In particular in time critical scenarios, such as emergency (crisis) management, browsing through documents to find the right information is not feasible. This is one of the main drivers for applying Semantic Web technologies in the decision support field, since general statistical approaches, such as free text search with Google-like methods, might not work well in such situations. Neural networks (2) have been used in many applications which are either close to statistics (e.g. interpreting time series data) or close to lower-level cognitive tasks, as in robotics or computer vision. They have proven less useful in higher level reasoning, probably due to the fact that scientists are still far away from being able to model the human brain with its many specialised and sophisticated cognitive functions. Regarding the modelling-in-the-large approach (3), we can mention the KRYPTON hybrid reasoning system, developed by Brachman, Gilbert, and Levesque, which popularized the term hybrid and the terms T-Box and A-Box (c.f. John Sowa’s discussion on Universal Basic Semantic Structures at ontolog-forum: ontolog-forum@ontolog.cim3.net (12 January 2012)). Cyc (c.f. cyc.com) is one the largest hybrid system, which uses heuristics in combination with various inference methods. In addition, KQML (Knowledge Query and Manipulation Language) is designed to support multiple interaction and dynamicity of agents that cooperate by message passing. In the DAML project proposal by Tim Berners-Lee, multiple heterogeneous agents were envisaged as the foundation for the Semantic Web (4). Tim Berners-Lee cited KQML as an example of what an agent language should support. Unfortunately, Tim Berners-Lee’s original idea about the role of the agents on the Semantic Web, has been dropped from the final DAML report. Meanwhile, much less tightly organized ideas such as Linked Open Data (LOD) have had a much faster uptake. The question of how to combine ideas from LOD with any of the many semantic structures is still a research topic. Finally, industry-led approaches under different usage paradigms (as explained in (5)) use a hybrid machinery to solve the search and navigation through the data sets. For example, the Question-Answering (QA) problem requires a machine to go beyond matching keywords in

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documents, which is what a web-search engine does, and correctly interpret the question to figure out what is being asked (Lally & Fodor, 2011). The QA system also needs to find the precise answer without requiring the aid of a human to read through the returned documents. To address these challenges, the research team at IBM developed a software architecture called DeepQA, on top of which IBM Watson is implemented. The DeepQA architecture assumes and pursues multiple interpretations of the question, generates many plausible answers or hypotheses, collects evidence for these hypotheses, and evaluates the evidence to determine if it supports or refutes those hypotheses (Ferrucci et al., 2010). IBM Watson contains hundreds of different algorithms that evaluate evidence along different dimensions. It utilizes Natural Language Processing (NLP) technology to interpret the question and extract key elements such as the answer type and relationships between entities. NLP is also used to analyse (prior to the competition) the vast amounts of unstructured text (encyclopaedias, dictionaries, news articles, etc.) that may provide evidence in support of the answers to the questions. Furthermore, IBM Watson’s NLP applies a parser that converts each text sentence into a more structured form (Cord, 1982). It also applies numerous detection rules that match patterns in the parse. These rules detect elements such as the focus of the question, the lexical answer types, and the relationships between entities in either a question or a potential supporting passage. To express pattern-matching rules over the parse trees and other annotations (such as named entity recognition results), IBM Watson uses Prolog language due to its simplicity and expressiveness. Besides QA, IBM Watson is also a strong player in Statistical Machine Translation (SMT) with many important innovations, such as the IBM word alignment models, BLEU score, IBM direct translation models. N. Bach discusses overlapping between IBM Watson DeeoQA and Statistical Machine Translation approach (Bach, 2011) as shown in Table 1: Table 1: Overlapping between IBM Watson DeepQA and Statistical Machine Translation

IBM Watson DeepQA Statistical Machine Translation

Content Acquisition Data Collection

Question Analysis Source-side Analysis

Hypothesis Generation Decoding

Soft Filtering Pruning

Hypothesis and Evidence Scoring System Combination

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Ranking N-best List Reranking

Confidence Estimation Confidence Score

The key distinction of IBM Watson regarding the way it processes input data is its Question Classification and Relation Detection feature (Question Analysis vs. Source-side Analysis as shown in Table 1). IBM Watson uses Question Classification to identify what types of question it has been asked; for example, is that a math question, puzzle, factual questions, or else. The question type triggers different models and strategies in the later processing steps. Current state�of�the�art SMT systems do not distinguish sentence types and domains. A similar model can be built, but to make it works, SMT systems may require additional training data and more time. In addition, IBM Watson uses Relation Detection throughout the question analysis process to understand whether the relations in the question are SVO (subject-verb-object) predicates or semantic relationships between entities. The author in (Bach, 2011) states: “…preserving semantic relations via translation process is interesting issue in which SMT may learn from Watson.” Over the past years, one of the leading ontology mailing lists called “ontolog-forum” has had heated debates on the relationships between classical Database Theory, Conceptual Modelling, Semantic Web, AI, Logic Programming and Cognitive Science. In those discussion threads, there is a rich repository of research strands that deserve re-evaluation, but there are no “takers”, because the people who discuss are either retired, or in other lines of work, while the younger generation of researchers sees no benefit in picking up research threads from 20 years ago. Therefore, our motivation in UNDERSTANDER is inspired by those AI approaches offering new possibility of combining BI and CI with Semantic Web and Conceptual Dependency (CD) theory, respectively.

1.2. Scope According to the CI taxonomy that is previously created by the group of CI experts (see Appendix 1), the outputs of CI can serve various purposes: (i) supporting different departments and their sales, (ii) strategy and portfolio definition; (iii) branding and differentiation; (iv) implementing go-to-market strategy; (v) launching new products; (vI) product management and development; (vii) customer analysis (or company overview analysis); (viii) performing business analysis. In UNDERSTANDER, we have no particular objective in mind. Rather, our aim is to pick a representative, but incomplete set of outputs that can be attributed to the above categories. If pushed for a specific answer, we would probably take product management and development, with some aspects of customer analysis. The aim would be for the user to have

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decision support for developing a future product portfolio, based on CI-related information as gathered by UNDERSTANDER’ agent system.

1.3. Structure of the Document Section 2 overviews related work in BI and CI. Section 3 describes our efforts to develop BI and CI domain vocabularies. Firstly, we describe three core technology pillars in UNDERSTANDER, such as (i) Business Intelligence Model (BIM), (ii) taxonomy of CI, and (iii) Conceptual Dependency (CD) theory. Secondly, we model UNDERSTANDER’s domain vocabulary for BI by implementing core elements (concepts and relationships) for each of the above mentioned three pillars. Implementation details in SWI-Prolog are given in section 3. Section 4 gives some conclusion remarks. The Appendices hold copies of important sources used in UNDERSTANDER, such as the CI taxonomy developed by a group of CI consultants, which is available at: http://www.mindmeister.com/18753652/competitive-intelligence. In addition, we give the full questionnaire to industrial partners on their BI/CI needs (Appendix 2).

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2. Related Work This section discusses related work in BI and CI.

2.1. Related Work in Business Intelligence The term Business Intelligence (BI) was introduced by Hans Luhn, IBM researcher (Luhn, 1958), who stated: “... [business] intelligence system … utilizes data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the “action points” in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points.” After Luhn’s very first definition on BI, in a period between 1958 and 1989, only a small number of papers on BI was published. In 1989, Gartner’s analyst Howard Dresner proposed using BI as an umbrella term to describe “concepts and methods to improve business decision making by using fact-based support systems”. From that time (1989), Gartner’s definition of BI became widely used as the primary definition (c.f. (Power, 2007), (Turban et al., 2007), (Turban et al., 2008), (Zeller, 2007)). Nowadays, BI can be defined as data technology that gathers and stores data, analyses data and delivers information (and/or knowledge), facilitates reporting, querying, and allowing organization to improve decision making (c.f. (Clark et al., 2007), (Kudyba & Hoptroff, 2001), (Michalewicz et al., 2007), (Moss & Shaku, 2003), (Negash, 2004), (Raisinghani, 2004), (Thierauf, 2001), (Turban et al., 2008)). BI can be also defined as the process that transforms data into information, and then into - knowledge (Golfarelli et al., 2004). The newly coined term BI 3.0, defined by Forrester Research’s James Kobielus, refers on the new software class bringing data discovery, advanced visualization, visual analytics, business discovery, self serve BI, and more (Cabiro, 2011). In addition to the typical functionality of multidimensional analysis (drill-down, drill-through, roll-up, sort, group, filter, calculation) some BI tools offer “what-if” scenario analysis, data animation and mobile capability. To understand the potential differentiation between BI 3.0 and its BI predecessors, we need to analyse the journey thus far (Gratton, 2012): “BI 1.0 and BI 2.0 were in essence conceptually similar to the Web 1.0 and Web 2.0 standards and this should come as no surprise as these standards focused on enhancing the experience and consistency of web interactions globally… BI 3.0 will focus on collaborative workgroups which are self-regulated (therefore self-governing in data management terms… ) and, which focus on information outcomes within the confines of core business interactions with customers, employees, regulators and third-parties.” To achieve BI 3.0, the new toolsets will need to be ubiquitous across devices, will need to hold people,

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process and data relationships within their design (semantics covering information context) and, to increasingly shield the business consumer from the complexity of the cloud and underlying systems. Figure 1 shows all three different stages of BI technological evolution (Gratton, 2012).

Figure 1. BI in a Nutshell (source: http://www.capgemini.com/sites/default/files/technology-

blog/files/2012/07/BI-3.01.png)

The rest of Section 2.1 discusses state of the art in Decision Support Systems and Semantic Web technologies, which are relevant to BI 3.0, as well as three mainstream approaches to BI: the traditional approach, the business processes approach, and the adaptive approach.

2.1.1. Decision Support Systems BI is rooted in the field of Decision Support Systems (DSS), although has additional strong associations with Knowledge Management (KM) and Competitive Intelligence (CI) (Clark et al., 2007), (Liebowitz, 2006), (Negash, 2004), (Turban et al., 2008), (Zeller, 2008). BI consists of a range of techniques intended to assist large organizations in determining the state and quality of their operations (Barone et al., 2010). It encompasses data and knowledge management, modeling of processes and policies, data quality, data privacy and security, data cleaning and integration, data exchange, inconsistency management, information retrieval, data mining,

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analytics, and decision support. Decision support is a field which is traditionally attributed to the social sciences, e.g., to support managers to make better decisions (Blomqvist, 2012). DSSs encompass wide area of applications ranging from BI, Information Retrieval (IR) and Knowledge Management, via general purpose social networking and collaboration support for group decision making, to situation awareness, emergency management, simulation systems, etc. DSSs have strong focus on models: some of key techniques in BI include multidimensional models, data cubes, and Online Analytical Processing (OLAP) (Power, 2002). Another field related to DSSs is IR, in which many early search engines and document-indexing approaches were originally targeted at Knowledge Management or managerial support within enterprises. According to (Power, 2008), DSSs can be divided into the following categories:

● Model-driven DSS (e.g., a financial decision support system predicting the impact of certain managerial decisions on the economical key indicators of the business using financial models),

● Data-driven DSS (e.g., file systems with search and retrieval capabilities), ● Communications-driven DSS (e.g., groupware and video-conferencing systems that

allow distributed and networked decision making), ● Document-driven DSS (e.g., document analysis and IR systems), and ● Knowledge-driven DSS (e.g., systems that recommend or suggest actions to the

users). According to (Arnott & Pervan, 2005)(Pervan & Arnott, 2006), DSSs can be classified based on the purpose of DSS rather than based on its internal structure. For example, DSSs can be classified into:

● Personal DSS (supporting individuals in decision-making), ● Group DSS (supporting a group of people making a joint decision), ● Negotiation DSS (supporting negotiation leading up to a decision situation), ● Intelligent DSS (processing data in a way to produce more meaningful information), ● Business Intelligence (BI) (data representing the state of an enterprise), ● Data Warehousing (a set of data sources that are integrated via unifying model), ● Knowledge-management DSS.

2.1.2. Semantic Web Technologies The Semantic Web shares many goals with DSS such as: being able to precisely interpret information, with the aim to deliver relevant, reliable and accurate information to a user when and where it is needed (Blomqvist, 2012). Semantic Web technologies have been used in DSSs

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during the past decade to solve a number of different tasks, such as information integration and sharing, web service annotation and discovery, knowledge representation and reasoning. These technologies refer to many areas, such as (Blomqvist, 2012):

● Semantic Web data (including representation languages, storage, search and querying of Semantic Web data, approaches for using or producing Linked Data, as well as quality assurance and provenance tracking of data),

● Ontologies and semantics (representation languages and patterns, engineering, management, retrieval and usage of Semantic Web ontologies and rules, reasoning services and rule execution engines),

● Semantic Web engineering and development (applications, methods, tools and evaluations of applications),

● Natural Language Processing (NLP) (machine learning and information extraction for the Semantic Web, population from text or from exploiting tags and keywords, or using semantic technologies to perform NLP),

● Social Semantic Web (social networks and processes, collaboration and cooperation, context awareness and user modelling, trust, privacy, and security),

● User Interfaces (interaction with and creation of Semantic Web data and models, information presentation, visualization and integration, personalization).

Semantic Web data can be utilized for DSS in several ways (Blomqvist, 2012). Some approaches use formats such as RDF and Web Ontology language (OWL) to integrate and allow access to data from existing data sources (Bhrammanee & Wuwongse, 2007) (Bell & Nguyen, 2010) (Bouamrane et al., 2009) (Abidi, 2008), while others focus on extracting Web data (Baumgartner et al., 2007) or utilizing Semantic Web data which are already in RDF, as an entirely new data source (Castellanos et al., 2011) (Terziyan & Kaykova, 2011), e.g., by incorporating Linked Data in their DSS application, or simply proposing to move from current data publishing principles to Linked Data (Nesic et al., 2011). There are also approaches for annotating and integrating models themselves, e.g., annotation of general data analysis and decision models (Deokar & El-Gayar, 2012) or business process models (Rao et al., 2012), as well as model integration and matching (Kaza & Chen, 2008), and transformation between models, e.g., from topic maps to ontologies (Matousek et al., 2011). Another examples of Semantic Web engineering tools that has impacted DSS are using triple stores as part of the DSS infrastructure (Ruttenberg et al., 2011) (Nebot & Berlanga, 2011), and the deployment of Service Oriented Architectures (SOA) enhanced by Semantic Web technologies (Kwon, 2006) (Moser et al., 2009) for sharing and accessing data. Some approaches even take step further and apply Semantic Web technologies in peer-to-peer networks, for interpreting messages, e.g., facilitating offers in a negotiation scenario (Du, 2009). The increased interest of hybridization between NLP and Semantic Web technologies has also been taken advantage for DSS (Castellanos et al., 2011) (Ruttenberg et al., 2011) (Huang & Tsai, 2011). However, the

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integration between Semantic Web and NLP is in its infancy. The intersection between DSS and the Social Semantic Web has been mainly focused on contextualizing data (Bose & Chen, 2009), utilizing social annotations such as tags (Dumontiera et al., 2010), and providing better decision support through advanced user modelling (Bose & Chen, 2009). Arguments on the social Web can also be used in decision-making contexts (Schneider et al., 2012). In addition, relevant approaches to DSSs and Social Semantic Web come from opinion mining (Pang & Lee, 2008), question answering and explanation (Moulin et al., 2002), contradiction detection (Ritter et al., 2008), controversy (Gyllstrom & Moens, 2011), persuasion (Young et al., 2011), stance detection (Walker et al., 2012), automatically typing links (Cleary & Bareiss, 1996). For example, opinion mining and sentiment analysis attempts to make automatic systems to determine human opinion from text written in natural language (Bhuiyan et al., 2009). Many researchers have worked on mining of sentiment bearing words (e.g., great, amazing, poor) and identifying their semantic polarity, such as positive, negative or neutral (Goldberg, & Zhu, 2006) (Lin et al., 2006). The authors in (Hatzivassiloglou & McKeown, 1997) identified several linguistic rules that can be exploited to identify opinion words and their orientations from a large corpus. This method has been applied, extended and improved in (Ding et al., 2008) (Kanayama & Nasukawa, 2006) (Popescu & Etzioni, 2005). Opinion mining trials in legal blogs and their potential impact on the law and the legal profession is further discussed in (Conrad & Schilder, 2007).

2.1.3. BI Approaches, Tools and Models (Azevedo & Santos, 2009) differs three mainstream approaches to BI, such as:

● the traditional BI approach - it is concerned with data aggregation, business analytics, and data visualization (Kudyba & Hoptroff, 2001), (Raisinghani, 2004), (Turban et al., 2008). This approach includes Data Warehouse (DW), Extract-Transform and Load (ETL), Online Analytical Processing (OLAP), Data Mining (DM), Text Mining, Web Mining, Data Visualization, Geographic Information Systems (GIS), and Web Portals;

● the business processes approach - it is focused on integration of business processes on BI (Eckerson, 2009), (Golfarelli et al., 2004), (Turban et al., 2008), (Wormus, 2008), (Zeller, 2007). This approach includes Business Performance Management (BPM), Business Activity Monitoring (BAM), Service-Oriented Architecture (SOA), Automatic Decision Systems (ADS), and dashboards.

● the adaptive BI approach - it is concerned with self-adaptive systems (Michalewicz et al., 2007).

Both social networking and social media brought new sets of information to more-traditional data that might fine-tune customers specific needs and support their competitive marketplace. New BI tools apply the semantic search and analytics technology (e.g. Oracle Social

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Engagement and Monitoring Cloud Service, which automatically captures consumer “considerations and preferences” metrics as well as insights from consumer-generated content in social media and structured and unstructured data environments (see (Oracle, 2012) for more details)). Some BI tools use keywords to analyze information, while others rely on NLP to disambiguate content. Some efforts have already resulted in standards such as OMG’s Business Process Modeling Notation (BPMN) (OMG BPMN, 2009). OMG’s Business Motivation Model (BMM) “defines the elements of business plan. [...] [contains] roles in the structure for three essential concepts: Business Process, Business Rule, and Organization Unit” (OMG BMM, 2010). Enterprise process modeling languages, design patterns (ontologies) and process frameworks have also been investigated for a long time (Barone et al., 2010). For example, TOVE (Fox, 1992), REA (McCarthy, 1982), TOGAF (TOGAF, 2009), Zachman’s Framework for Enterprise Architecture (Zachman, 1987), yet recently influence work on Service-Oriented Architectures (SOAs). Finally, Business Intelligence Model (BIM) (Barone et al., 2010) provides a set of constructs for modeling and analysis of a business context consisting of intentions, situations, processes, actors, influences, key performance indicators, and more. It is intended to support the process of modeling and analysis of a business organization at both strategic and tactical level. BIM is grounded on modeling techniques from diverse sources. For example, BIM includes:

● DOLCE foundational ontology to describe abstract concepts (Gangemi et al., 2002); ● Goal-Oriented Requirements Engineering and its concepts to express the intentional and

social concepts (Dardenne et al., 1993) (Yu, 1997); ● Influence diagrams to define the notion of influence (Howard & Matheson, 1984), ● OMG’s BMM standard for concepts related to SWOT analysis (Dealtry, 1994).

More details on BIM core concepts and the abstraction mechanisms is given in Section 3.

2.2. Related Work in Competitive Intelligence BI is closely associated with CI, and Knowledge Management Systems (KMS) (c.f. (Clark et al., 2007), (March & Hevner, 2007), (Negash, 2004), (Thierauf, 2001), (Turban et al., 2008)). For example, Negash presents CI as a branch of BI, and refers to it as “a systematic and ethical program for gathering, analyzing and managing external information that can affect company’s plans, decisions and operations” (Negash, 2004). (Michaeli, 2012) defines CI as “an analytical process which transforms disaggregated company, industry and market data into actionable strategic knowledge about the position, performance, capabilities and intentions of target companies.” CI is concerned by the gathering and analysing of intelligence describing the behaviour of the market, and competitors on the market. Wikipedia lists CI as “the action of gathering, analyzing, and distributing information about products, customers, competitors and any aspect of the environment needed to support executives and managers in making strategic

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decisions for an organization.” (Herring, 2007) summarizes set of “measures of effectiveness” (MOEs) that had been previously developed and used by professionals in the commercial IT field, and now reapplied for the purpose of CI. It includes the following:

● Time saving: savings for both professional and support personnel; ● Cost savings: elimination or reduction in expenses; ● Cost avoidance: elimination of planned expenses; ● Revenue increases: increases in the number of sales or size of sales; ● Value added: benefits that are not easily related to specific dollar values, such as more

effective strategies or better new products and services. Regarding the CI technology tools used so far, (Muller, 2007) refers on the Global Intelligence Alliance (GIA) survey (GIA, 2007) as a reference on CI organizations, activity areas, tools, education, and challenges. “According to the GIA survey [...] the use of technology to support CI activities varies across market.” There are tools to assist in building networks, communicating intelligence, maintaining internal contacts with clients and information sources, as well as tools to assist in the collecting and processing of information (e.g. search and retrieval software), and reporting intelligence (e.g. emails, Internet). To analyse information, most companies use competitor analysis and SWOT analysis. Other tools cover financial analysis, profit and loss analysis, industry analysis, customer segmentation, etc. (Liebowitz, 2006) and (Azevedo & Santos, 2009) stress that CI, BI, KMS, and AI should be aggregated “to provide value-added information and knowledge toward making organizational strategic decisions” in order to achieve Strategic Intelligence for businesses.

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3. Domain Vocabulary for Business Intelligence The authors in (Horkoff et al., 2012) stated that although BI systems are already widely used in many companies, data still remains the main traps when it comes to their analysis and interpretation: “(a) Systems are still very technical and data-oriented, (b) hard to understand what the data means, (c) hard to design queries or make new reports without technical knowledge or a knowledge of the underlying data structure, (d) gap between business and IT-supplied data.” Gap between business and data remains the greatest barrier to the adoption of BI technology, presenting the greatest factor in the cost of applying BI technology (Barone et al., 2010). Research by Mylopoulos and his team on raising the level of abstraction of BI systems through a special modeling language, which brings concepts more familiar to business, such as strategic models, business models and strategies, business processes, markets, trends and risks, is known as - the Business Intelligence Model (BIM). BIM becomes a part of the Business Intelligence Network, a Canadian project for the definition of the next generation of BI technologies (for more details see: http://bin.cs.toronto.edu/home/index.php). BIM modeling techniques are based on several sources (Barone et al., 2010). For example, it consolidates languages and technologies for capturing business strategy such as:

● Strategy Maps and Balanced Scorecards (by Kaplan & Norton), ● Business Motivation Model (by OMG), ● Dynamic SWOT (Strength, Weakness, Opportunity, Threat) Analysis (Dealtry), ● Goal models.

In addition, BIM deals with several reasoning techniques such as:

● Goal Model Reasoning, ● Probabilistic Decision Analysis, ● Reasoning with Indicators, ● Hybrid Reasoning (with incomplete indicators).

Abstract concepts for describing “things” in BIM are inspired by the DOLCE foundational ontology (Gangemi et al., 2002). The intentional and social concepts in BIM are adopted from concepts in Goal-Oriented Requirements Engineering (Dardenne et al., 1993) (Yu, 1979). The notion of influence is adopted from influence diagrams (Howard & Matheson, 1984), which is a well-known and accepted decision analysis technique. Concepts related to SWOT analysis (Dealtry, 1994) and others have been adopted from OMG’s Business Motivation Model standard

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(OMG BRG, 2007). We take BIM into consideration for the development of BI related domain vocabulary in UNDERSTANDER. In Section 3.1, we firstly analyse three technology pillars, such as BI, CI and CD theory. In Section 3.2, we present the way in which UNDERSTANDER domain vocabulary is developed in SWI-Prolog. We choose SWI-Prolog because it provides a stable environment for knowledge representation with actionable semantics. It is also capable of providing Web services, and generating HTML pages dynamically, as well as providing data for web applications (c.f. http://www.swi-prolog.org/web/).

3.1. UNDERSTANDER Technology Pillars

3.1.1. Business Intelligence Model (BIM) BIM’s language semantics incorporates Description Logic (DL) in a way that makes it easily extensible. It also allows for publishing of generic BIM models as ontologies on the Semantic Web (Horkoff et al., 2012). For example, BIM formal definition is implemented in Web Ontology Language (OWL), and encoded in Protege tool. BIM is intended to support the modeling and analysis of a business organization at both a strategic and a tactical level. It provides a set of constructs for modeling and analyzing a business context including intention, situation, processes, actors, influences, key performance indicators, and more (Barone et al., 2010). Figure 2 shows the BIM upper level taxonomy. Section 3.2.1. (“Implementing BIM Concepts and Relationships in SWI-Prolog”) explains in more details concepts (classes), and their relationships, which have been adopted from BIM.

Figure 2. The BIM Upper Level Ontology (source: (Barone et al., 2010))

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3.1.2. Taxonomy of Competitive Intelligence Our objective in UNDERSTANDER is to provide domain vocabulary to support both BI and CI. Hence, we have adopted taxonomy of CI that was jointly developed by the group of CI analysts, which is publicly accessible from the following source: http://www.mindmeister.com/18753652/competitive-intelligence (password: editme). Figure 3 shows the basic concepts of the CI taxonomy.

Figure 3. The CI Taxonomy

More detailed overview of this taxonomy is given in Appendix 1. The implementation details about the integration of CI within UNDERSTANDER domain vocabulary will be discussed in Section 3.2.2. “Implementing CI Concepts and Relationships in SWI-Prolog”.

3.1.3. Conceptual Dependency Theory Conceptual Dependency (CD), as proposed by Schank & Abelson in 1975, is a theory about verb-oriented organisation of knowledge (Schank & Abelson, 1975). It discusses representation of the meaning of natural language sentences in a way that facilitates for drawing inferences from the sentences. In addition, CD introduces the idea of a canonical meaning representation without duplicating information. For example, (a) different words and structures represent the same concept, (b) language-independent meaning representation. Recently, Lytinen have summarised the work of Schank & Abelson, and their successors in (Lytinen, 1992). In UNDERSTANDER, we briefly summarize the main aspects of Schank & Abelson’s CD theory. For more information we refer the reader to Lytinen’s survey, which

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emphasizes that a richly structured knowledge base and relatively simple processing rules are prerequisites for CD. Intriguingly, Tom Gruber and his colleagues took a similar approach when developing the SIRI speech-driven assistant that is used in Apple’s new iPhones. “Thus, perhaps the most important common thread that connects the CD family of research is the assumption that intelligence is knowledge-intensive, rather than processing-intensive. If the representations used to perform a task are rich enough, then processing strategies traditionally associated with AI programs, such as search, become less important. The result is simpler processing theories which are highly dependent on a very rich, and highly organized, knowledge base.” (Lytinen, 1992) CD theory describes 11 primitives for representing the semantics of actions. Table 1 shows the slot structure of these primitives. Physical Actions:

● INGEST: to take something inside an animate object; ● EXPEL: to take something from inside an animate object, or force it out; ● GRASP: the grasping of an object by an actor so that it may be manipulated; ● MOVE: the movement of a body part of an agent; ● PROPEL: the application of physical force to an object;

State Changes: ● PTRANS: to change the location of a physical object; ● ATRANS: to change an abstract relationship of a physical object;

Mental Actions: ● MTRANS: to transfer information between agents mentally; ● MBUILD: to create or combine thoughts or new information by an agent;

Instruments for other Actions: ● ATTEND: the act of focusing attention of a sense organ toward an object; ● SPEAK: the act of producing sound, including non-communicative sounds.

In addition, CD theory differs between the following conceptual categories:

● PP - picture producer; physical object; ● ACT - one of eleven primitive actions; ● LOC - location; ● T - time; ● AA - action aider; modifications of features of an ACT; ● PA - attributes of an object of the form STATE(VALUE).

We will not enter the debate whether CD theory is cognitively plausible or linguistically

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acceptable. In UNDERSTANDER, we are using CD theory as a starting point to explore the more practical engineering question: “Is CD capable of addressing some questions of machine understanding in the context of web content management and can it be a practical basis for web application developers who need to introduce semantic descriptions of complex themes into their content related web applications”? In other words, we can ask the question: “Does CD help in adding relatively sophisticated semantic descriptions to web content”? Table 1: Slot structure of the primitives of CD Theory

CD Primitive Subject Object Secondary Object

Tertiary Object

PTRANS (flying, walking)

Actor Object From

(location) To (location)

ATRANS Actor Object From (Actor) To (Actor)

MTRANS (reading)

Actor MentalObject From (Actor) To (Actor)

MBUILD Actor MentalObject FromSource *ViaInstrument

ATTEND Actor ToSomeObject - -

GRASP Actor SomeObject - -

PROPEL Actor PhysicalObject - *ViaForceType

MOVE Actor BodyPart - -

INGEST Actor Object - -

EXPEL Actor Object - -

SPEAK Actor SoundObject - -

Note that parameters annotated with an asterisk (*) were added by us. In the following, we present several examples of CD primitives in use. Instead of using CD’s native graphical representation for conceptualizations and their dependencies, we present CD primitives in Prolog-like notation.

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Example 1: “Andrew bought an iPhone from Bertrand for 100 Euro” The pattern for buying is the two-way ATRANS of possession between two actors:

ATRANS(past, Andrew, money(Euro, 100), Andrew, Bertrand), ATRANS(past, Bertrand, artefact(iPhone), Bertrand, Andrew).

Example 2: “Joe is interested in buying a new drum set for his daughter, Janet”. Joe builds the mental state (by himself) of ATRANSing money for drum set and then ATRANSing the drum set to Janet, and that mental state is a CD-plan. Note that we have not added the information that Janet is Joe’s daughter.

MBUILD(Joe, (ATRANS(future, Joe, money(?Amount, ?Currency), Joe, ?Seller), ATRANS(future, ?Seller, artefact(drum-set), ?Seller, Joe), ATRANS(future, Joe, artefact(drum-set), Joe, Janet)), Joe, plan).

3.2. UNDERSTANDER Domain Vocabulary for BI UNDERSTANDER domain vocabulary is based on BIM and CI taxonomies. It also provides alignments towards basic primitives of CD theory. Therefore, the development of UNDERSTANDER domain vocabulary includes the following three phases: (a) implementation of BIM core concepts and relationships in SWI-Prolog, (b) addition of CI concepts and relationships, and (c) addition of CD concepts and relationships.

3.2.1. Implementing BIM Concepts and Relationships in SWI-Prolog BIM taxonomy includes core concepts from several existing languages and techniques for capturing business strategy, such as Kaplan & Norton’s Strategy Maps and Balanced Scorecards, OMG’s Business Motivation Model, Dealtry’s Dynamic SWOT Analysis, Goal Models (Barone et al., 2010). BIM also uses several reasoning techniques, such as Goal Model Reasoning, Probabilistic Decision Analysis, Reasoning with Indicators, Hybrid Reasoning (with incomplete indicators). Figure 4 presents BIM Upper Level Ontology (Horkoff et al., 2012) (see Figure 2 for more abstract view on BIM Upper Level Ontology). For example, Goal is an objective of a business; Situation reflects internal or external factors influencing fulfillment of goals; Indicator shows performance measure of strategic activities, etc.

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Figure 4. BIM Upper Level Ontology (source: (Horkoff et al., 2012))

Encoding BIM Upper Level Ontology into SWI-Prolog means defining its common components such as: classes (concepts), attributes (properties, features), relations, individuals (instances), function terms, restrictions, rules, axioms, and events. Relations represent ways in which concepts and individuals relate to each other. Restrictions are descriptions of what must be true in order for some assertions to be accepted as input. Rules are statements describing the logical inferences that can be drawn from an assertion in a particular form. Axioms are assertions in a logical form, describing the application domain of an ontology. Events represent the changing of attributes or relations. The BIM Upper Level Ontology contains basic_concept and has_subconcept. Each basic_concept consists of a quantity of has_subconcept, such as situation, which further consists of two subclasess namely, goal and organizational_situation. Basic concepts are not made up of any smaller concepts, and they could be combined with other basic concepts to form subconcepts. We represent basic concepts in SWI-Prolog as facts. For example: basic_concept(situation). basic_concept(goal). basic_concept(organizational_situation). Subconcepts can be presented as the name of the subconcept followed with a list of the basic concepts. For example, the following fact tells that a situation is made up of two basic concepts (subclases) such as goal and organizational_situation:

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has_subconcept(situation,[goal,organizational_situation]). Here is an excerpt from the database of has_subconcept describing BIM: has_subconcept(business_schema_thing,[indicator,situation,task,entity,

proposition]). has_subconcept(situation,[goal,organizational_situation]). has_subconcept(entity,[actor,process,action,resource]). has_subconcept(actor,[position,role,agent]). has_subconcept(proposition,[directive,intention,domain_assumption]). has_subconcept(intention,[qualitative_intention,quantitative_intention

]). has_subconcept(business_schema_relationship,[influence,refines,measure

s,evaluates]). Figure 5 shows BIM concepts in a tree hierarchy.

Figure 5. Tree knowledge hierarchy of BIM We can also add new relationships to describe BOM in more details. For example, we can write the program that, given certain concept, lists all basic concepts required to construct it. If the concept X is a basic concept, then nothing more is required, and we only define the boundary condition: /* if X is a basic concept (the boundary condition) */ partsof(X,[X]) :- concept(X). If the concept X is a subconcept, we define two relations: partsof to find out if there is a matching subconcept fact in the database, and partsoflist for each member of the list of subconcepts: /* if X is a subconcept) */ partsof(X,P) :- has_subconcept(X,Subconcept),

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partsoflist(Subconcept,P). partsoflist([],[]). partsoflist([P|Tail],Total) :- partsof(P,Headparts), partsoflist(Tail,Tailparts), append(Headparts,Tailparts,Total).

3.2.2. Implementing CI Concepts and Relationships in SWI-Prolog CI taxonomy consists of several core concepts, such as: CI skills, CI outputs, CI ecosystems, and CI allied disciplines:

● CI allied disciplines concept lists several relevant fields, e.g. market intelligence, market research, knowledge management, etc.

● CI ecosystem concept considers the main elements of client side, vendor side, various organizations and associations.

● CI decision support concept lists various decision support systems, e.g. strategy & portfolio, branding, customer analysis, etc.

● CI skills concept classifies existing approaches to applying CI such as (a) researching CI and (b) analysing CI.

Table 2 shows our approach to functional alignment between newly constructed CI taxonomy and BIM Upper Level ontology. Table 2: Taxonomy alignment between CI taxonomy and BIM Upper Level ontology

CI taxonomy BIM Upper Ontology Comment

CI ecosystem Entity

CI allied

disciplines BusinessSchemaThing/

Entity/

EntityCategory

EntityCategory is a newly added concept

CI decision

support BusinessSchemaThing/

Situation

CI skills Business Schema

Relationship/Business

Schema Analyses

BusinessSchemaAnalyses is a newly added concept

The above taxonomy alignment includes the following steps:

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Firstly, by CI ecosystem concept, we classify various entities such as clients (sales, marketing, library, etc.), vendors (small firms, major consultancies, independent contractors, etc), organizations and associations. Therefore, we provide alignment between CI ecosystem (from CI taxonomy) and the Entity class (from BIM ontology). Secondly, CI allied

disciplines classifies common CI areas such as economic espionage, corporate security, market intelligence, market research, etc. This information gives more abstract view on previously defined entities, and can be expressed by adding a new subconcept of Entity, called EntityCategory. Thirdly, CI decision support lists common ways of supporting CI, such as branding, go-to-market, customer analysis. Hence, we map that concept with the BIM’s Situation concept. Finally, CI skills is the most important concept that lists various analytical tools and methods used to support enterprise, finance, evolutionary analysis, environmental analysis, etc. In order to encode this concept, we extend BIM Upper Ontology by adding a new subconcept of BusinessSchemaRelationship, namely BusinessSchemaAnalyses, which further includes subconcepts such as CIFinancialAnalysis, CIEvolutionaryAnalysis, CIEnvironmentalAnalysis,

CIEnterpriseAnalysis, CICompetitiveAnalysis. Figure 6 shows a new knowledge hierarchy, after merging BIM and CI taxonomies.

Figure 6. Tree knowledge hierarchy after merging two taxonomies Encoding CI concepts into previously constructed knowledge base in SWI-Prolog, adds the following concepts: /* UNDERSTANDER CI concepts */ basic_concept(entity_category). basic_concept(business_schema_analyses).

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basic_concept(ci_financial-analyses). basic_concept(ci_enterprise-analyses). basic_concept(ci_evolutionary-analyses). basic_concept(ci_environmental-analyses). basic_concept(ci_competitive-analyses). In addition, a new subconcept business_schema_analyse and its elements are also added: subconcept(business_schema__analyse,

[ci_financial_analyses, ci_enterprise_analyses, ci_evolutionary_analyses, ci_competitive_analyses, ci_environmental_analyses]).

Each of the above elements of business_schema__analyse concept contains various elements describing particular types of analysis. For the full view of CI taxonomy and its elements, see Appendix 1. An excerpt from the knowledge base in SWI-Prolog is as follows: /* ci_financial_analyses */ basic_concept(analysis_of_competing_hypothesis). basic_concept(competitor_cash_flow_analysis). basic_concept(financial_ratio_and_statement_analysis). basic_concept(statistical_analysis). basic_concept(linchpin_analysis). basic_concept(strategic_funds_programming). basic_concept(sustainable_growth_rate_analysis).

3.2.3. Implementing CD Concepts in SWI-Prolog The core concepts in CD theory are CD primitives and CD conceptual categories:

● CD primitives are used for representing semantics of possible actions, such as INGEST, EXPEL, GRASP, MOVE, PROPEL, PTRANS, ATRANS, MTRANS, MBUILD, ATTEND, SPEAK;

● CD conceptual categories are location, time, physical object, etc. Encoding CD theory into SWI-Prolog statements requires defining its common components as a part of our knowledge base. Therefore, in Table 3, we show taxonomy alignment (at the schema level) between CD concepts and BIM Upper Level ontology. Table 3: Taxonomy alignment between CD taxonomy and BIM Upper Level ontology

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CD theory concept BIM Upper Ontology Comment

CD primitives BusinessSchemaThing/ Task

CD conceptual categories/ PP

BusinessSchemaThing/ Entity/ Actor

it stands for “picture producer”, or “physical object”

CD conceptual categories/ ACT

BusinessSchemaThing/ Entity/ Action

it stands for the one of eleven primitive actions

CD conceptual categories/ LOC

BusinessSchemaThing/ Task/ Location

Location is a newly added concept

CD conceptual categories/ T

BusinessSchemaThing/ Task/ Time

Time is a newly added concept

CD conceptual categories/ AA

BusinessSchemaThing/ Task/ Resource

it stands for “action aider”

Figure 7 shows new knowledge hierarchy of UNDERSTANDER vocabulary, which now merges BIM, CI, and the core concepts of CD theory (note newly added concepts such as location_ and time_ which are coloured in blue).

Figure 7. Tree knowledge hierarchy in UNDERSTANDER after merging BIM, CI and CD

Encoding new CD concepts into UNDERSTANDER knowledge base into SWI-Prolog adds the following concepts and subconcepts: /* UNDERSTANDER CD concepts */

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basic_concept(cd_time). basic_concept(cd_location). has_subconcept(task, [cd_time,cd_location]).

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4. Conclusion This report discusses our approach to modeling and implementation of UNDERSTANDER domain vocabulary for BI. The presented domain vocabulary is grounded on three BI-related technology pillars, such as BI, CI, and CD theory, and is implemented in SWI-Prolog. It is the base for further development of BI knowledge base (WP4), as well as for the implementation of BI seekers - user agents and its domain-specific priming scripts for BI (topics of both WP2 and WP3). In UNDERSTANDER, we also want to differ between real world situations and web content. Hence, the aim of our further work will be to develop a reasoner that embodies some real world knowledge, and is additionally able to categorise web content according to given world knowledge.

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Appendix 1 - Mind Mapping’s Taxonomy of CI Taxonomy of CI (source: http://www.mindmeister.com/18753652/competitive-intelligence) (password “editme”) Ecosystems

● Client side = Sales, Marketing, Strategy, IT, dedicated CI department, Library ● Vendor side = Small firms, Independent contractors, Major Consultancies ● Academic = Research, Teaching ● Organisations = SCIP, SLA, Quirks, Social networking sites = Ning CI group,

LinkedIn Groups, Facebook ● Associations = SLA, AMA, ESOMAR

Outputs

● Field/Sales Support ● Strategy and Portfolio ● Branding and Differentiation ● Go-to-market, Product Launch ● Product management / development ● Customer analysis = Company overview analysis, Lines-of-Business Analysis,

Management Profiles ● HR/Recruiting ● Legal

Skills/Primary Research

● Phone interviewing / IDIs ● Trade show intelligence ● Environmental research ● networking

Skills/Secondary research

● Public sources ● Gray Lit / Deep web ● proprietary databases ● social network analysis ● financial analysis

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Skills/Analysis/Competitive ● Benchmarking ● BCG growth (aka product portfolio) matrix ● competitive positioning ● customer segmentation and needs analysis ● customer value analysis ● GE business screen (?) ● Industry Analysis (Porter’s 5 Forces) ● Industry Analysis / Nine Forces ● Strategic group analysis

Skills/Analysis/Enterprise

● Blindspot analysis ● Business Model Analysis ● Competitor Analysis ● Functional capability and resource analysis ● Management profiling ● McKinsey 7s Analysis ● SERVO Analysis ● Product Line analysis ● Shadowing analysis ● Supply Chain analysis ● SWOT analysis ● Value chain analysis ● Win/Loss analysis

Skills/Analysis/Environmental

● Country Risk analysis ● Corporate Reputation analysis ● Critical Success Factor analysis ● Driving Forces analysis ● Issue analysis ● Macro-environmental (STEEP) analysis ● Political and country risk analysis ● Scenario analysis ● Stakeholder analysis ● Strategic Relationship analysis

Skills/Analysis/Evolutionary

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● Event and Timeline analysis ● Experience curve analysis ● Growth vector analysis ● Historiographical analysis ● Indications and Warnings analysis ● Patent analysis ● Product life cycle analysis ● S-curve (technology life cycle) analysis ● Technology forecasting ● War gaming

Skills/Analysis/Financial

● Analysis of competing hypotheses (ACH) ● Competitor cash flow analysis ● Financial ratio and statement A. (including disaggregated financial ratio analysis) ● Interpretation of statistical analysis ● Lynchpin analysis ● Strategic funds programming ● Sustainable growth rate analysis

Allied disciplines:

● MLS ● IT ● Market Intelligence ● Knowledge Management ● Market Research (quantitative & qualitative) ● economic espionage ● corporate security ● public service (law enforcement, national intelligence service, local intelligence

service) ● Forensic accounting

Appendix 2 - Questionnaire on BI/CI Industry Needs The questionnaire is available online from the following link: http://www.surveygizmo.com/s3/1462014/Questionnaire-for-BI-CI-Industry-needs. It collects the answers on current business practice in acquiring business and competitive intelligence:

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(1) Which kind of knowledge about competitors is most important to you and how happy are you with your current methods and tools for obtaining that knowledge? (2) How important are the different sources from which you develop a picture of your position in the market, with respect to the competition, and how well do these sources perform? (3) In which sector is your business? Please give a short free-form description. (4) What is your annual turnover? (5) What is the number of employees (estimate) that generate this turnover? (6) How many customers (estimate) do you need to generate 80% of your turnover? (7) How many sales items or units (estimate) do you produce per year? (8) If you had to compare your company with your competitors, what categories/words would you choose for the headings of the report? Please give us 5 to 10 categories that you consider important. (9) Would you like to receive a copy of the answers you have given us in this survey? We will need a valid email address from you, and the address is ONLY used to send you the copy of your answers!

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