D. Lenat and R.V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project, AddisonWesley, Reading, Mass., 1990.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....attribution, reason explicitly about whether the person in question has access to that same source. We will call these partitions of the system s knowledge environments (the term contexts is also commonly used) This kind of partitioning can be motivated by various independent needs: in Cyc [10], propositions are grouped into environments based on common implicit assumptions, as a way of maintaining a kind of consistency across a large knowledge base; in de Kleer s ATMS [2] an environment is a set of propositions that are true given a particular set of explicit assumptions, and the ....

D. B. Lenat and R. V. Guha. Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project. Addison-Wesley, Reading, Massachusetts, 1990.

....and if a rule is able to interpret the affixing of a parenthesis. We are currently working on this delicate aspect in order to unify all the rules we have accumulated for resolving concrete cases. We are aware that these types of inference are comparable to the micro theories of the Cyc project [8] in which the need for a great amount of information is the main thesis. We will see in section 5.2.1 that the local con text may categorise an unknown proper name and therefore it may help to desambiguate an ambiguous known proper name. For instance, if the text speaks of the mayor of St Louis, ....

LENAT D., GUHA R. 1990 Building large Knowledge-based Systems : Representation and Inference in the Cyc Project, AddisonWesley

.... as a car not starting) has many possible explanations, ranging from plausible (out of gas) to implausible (wrong car) to downright ridiculous (starter motor stolen by aliens to repair spaceship) To reason with a lot of general knowledge imagine a probabilistic knowledge base as large as Cyc [11] it helps to be able to work with a plausible subset. But if this subset is selected in advance, we cannot handle situations where implausible rules suddenly become plausible, for example, if the car contains unfamiliar belongings or small grey men with large eyes. Knowledge based systems could ....

DB Lenat and RV Guha. Building large knowledge-based systems: Representation and inference in the Cyc project. addison Wesley, 1990.

....1 Introduction Truly intelligent action requires large quantities of knowledge. Acquiring this knowledge has long been the major bottleneck preventing the rapid spread of AI systems. Two main approaches to this problem exist today. In the manual approach, exemplified by the Cyc project [10] , human beings enter rules by hand into a knowledge base. This is a slow and costly process. Although the original goal was to complete Cyc in ten years, it has now been under development for seventeen. In the machine learning approach, exemplified by 0 Permission to make digital or hard ....

D. B. Lenat and R. V. Guha. Building Large KnowledgeBased Systems: Representation and Inference in the Cyc Project. Addison-Wesley, Reading, MA, 1990.

....these objects and the relations that exist over these objects; these will be represented by predicates in our language. We next define a set of axioms in first order logic to represent the constraints over the objects and predicates in the ontology. This set of axioms constitutes a microtheory ( Lenat Guha 90] and provides a declarative specification for the various tasks we wish to model. Intuitively, the axioms in the microtheory enable the model to deduce answers to questions that one would normally assume can be answered if one has a conmon sense understanding of the enterprise. To formalize ....

Lenat, D. and Guha, R.V. Building Large Knowledge-based Systems: Representation and Inference in the CYC Project. Addison Wesley, 1990.

....of current applications. 4.1 OpenCyc The Cyc system is the most ambitious knowledge representation project understaken to date. It has been in development since 1984, originally as part of Microelectronics and Computer Technology Corporation (MCC) but later as a separate company called Cycorp [16, 17]. The full Cyc KB is proprietary which has hindered it s adaptation in natural language processing. However, portions 7 of the KB have been released for public use to encourage broader usage. For instance, there is now an open source version of the system called OpenCyc (www.opencyc.org) which ....

Lenat, D.B., Guha, R.V.: Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project. Addison-Wesley, Reading, Massachusetts (1990) 13

....enumerate all the possible ways the world can change, we are in trouble. This is especially true if we need to invoke common sense reasoning (such as dead people don t need visas) as part of the process. The complexity of representing common sense knowledge has kept AI researchers busy for decades [11], and is still a research problem. More specifically, there appears to be no formal method for enumerating a complete set of external events that might have a substantial impact on a workflow in process. At this point in the analysis, we have identified that publish subscribe technology could ....

D. Lenat and R. Guha, Building Large Knowledge-based Systems: Representation and Interface in the Cyc Project. Reading, MA,: Addison-Wesley, 1990.

....a detailed description of specifying these contructs in LGAccess. 7. EXPERIMENTAL RESULTS A preliminary version of the system is already available and providing very promising results. Consider again the Example 5. We used an excerpt of the freely available CYC Geography ontology on the Web at [24] that contains among others information about geopolitical entities, i.e. countries, states, cities, etc. For the instance data, we used the Terra and the GlobalStatistics data sources. We located the Geography ontology as well as the Terra data source at the local mirkwood server, and the ....

D. B. Lenat and R. V. Guha. Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project. Addison-Wesley, 1990.

....to many common sense questions. The artificial intelligence and knowledge management communities have contributed a lot in developing enterprise ontologies based on DEM as a result of efforts in achieving common sense reasoning on knowledge bases and agent communication. The CYC project at MCC [38], 5] the Enterprise Project at University of Edinburgh [12] and the TOVE project at University of Toronto [63] are three noticeable projects in the area of DEM. 32 In early 1980 s one of the recognizable efforts for creating industry wide standard for enterprise modeling can be found in US Air ....

Lenat D., and Guha R.V., "Building Large Knowledge Based Systems: Representation and Inference in the CYC Project", Addison Wesley Pub. Co 1990.

.... of progress in solving the D f s f ef( #D problem generally led AI researchers to either abandon inferential and knowledge based approaches in favor of more quantitative approaches (e.g. 14] or to focus almost exclusively on the development of large commonsense knowledge bases (e.g. [15]) Within linguistics and formal semantics, one the other hand, little or no attention was paid to the issue of commonsense reasoning at the pragmatic level. Indeed, the prevailing wisdom (which might be partly due to lack of progress in AI based NLU) was that a number of NLU tasks require the ....

....(see [25] that these inferences do not always require the storage of and reasoning with a vast amount of background knowledge, it is clear that a number of tasks do require such a knowledgebase. Indeed, substantial effort has been made towards building ontologies of commonsense knowledge (e.g. [15,26,27]) and a number of promising trends that advocate ontological design based on sound linguistic and logical foundations have started to emerge in recent years ( 28,29] However, a systematic and objective approach to ontological In [25] we suggest an inferencing strategy that models individual ....

Lenat, D. B. and Guha, R.V. (1990), Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project. Addison-Wesley. 19

....(congruency tests) The structure of the three levels including a small set of examples is shown in figure 4.1. 4.1 Epistemic Ontological Level A taxonomy of disjunctive sorts can and should only provide a rather global classification into categories of primary importance. These categories have l[Lenat and Guha, 1990] 12 4.1. EPISTEMIC ONTOLOGICAL LEVEL 13 epistemic ontological number quantity measurement specifier quality graduator operational quality event facts state abstract altribute entity abstracted fact concrete continuous entity entity discrete entity sorts ....

D. B. Lenat and R. V. Guha. Building large knowledge based systems: representation and inference in the Cyc project. Addison Wesley, Reading (Mass.), 1990.

....make such systems intractable and in compromises to make it possible to live with the their worst case intractability [5, 23, 35] In addition there have been four other developments. Lenat took on the immense task of attempting to represent all of the common sense knowledge in the Cyc project [15, 21], opinions of the success of which vary widely but which attempted a far wider brief than just terminology. Quite separately Sowas Conceptual Graph notation for logic gained an increasing following as an interlingua linguistic and terminological representations, although no completely ....

D. B. Lenat, R. V. Guha, Building Large Knowledge-Based Systems: Representation and inference in the Cyc Project. (Addison-Wesley, Reading, MA, 1989).

....in [Bernus et al. 96] The US Department of Defense has also embarked upon the creation of a DOD wide GEM [DOD 93] The development of ontologies for Enterprise Models (DEMs) is more recent 1. There are a few projects whose scope of modelling is rather broad, including the CYC project at MCC [Lenat Guha 90] the TOVE project at the University of Toronto [Fox et al. 93] and the Enterprise project at the University of Edinburgh [Us chold et al. 97] What is modelled of an enterprise can be divided into categories. The following identifies a subset of these categories and ontologies being developed ....

Lenat, D., and Guha, R.V., (1990), Building Large Knowledge Based Systems: Representation and Inference in the CYC Project. Addison Wesley Pub. Co.

.... ontology is a terminological ontology whose categories are distinguished by axioms and definitions stated in logic or in some language that could be automatically translated into logic [11] Examples of axiomatized ontologies include the GALEN core model [12] the PSL ontology [13] and Cyc [14]. Our current work is motivated by the need of new tools that can improve the retrieval and integration of information. In this work we focus on ontologies whose specification components include entity classes, semantic relations among these classes, and distinguishing features that describe ....

Lenat, D. and R. Guha, Building Large Knowledge Based Systems: Representation and Inference in the CYC Project. 1990, Reading, MA: Addison-Wesley Publishing Company.

....complex database level queries and that provide the user with intuitive, high level access to desired data. Automated Query Generation Various approaches to providing high level query mechanisms have been explored. Early on, the problem was addressed by the Universal Relation (UR) theory [16][21] 39] which provides programmers and users with a single table view of the database. High level queries are formulated to the database table, while an underlying query engine creates a database level query based on the relationships defined by the conceptual representations of the databases. ....

....that ad hoc queries are not easily answered. Carnot [7] provides a layered, service oriented approach to achieving and managing integrated databases. Integral to Carnot is an enterprise modeling and integration facility and a knowledge discovery facility. The core knowledge base of Carnot is Cyc [16], which is semantic network that describes the global context of the domain of query discourse. To be precise, the purpose of Cyc is to describe the entire domain of human discourse. The model integration 16 The Handbook of Software Engineering and Knowledge Engineering facility automates the ....

D. B. Lenat and R. V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project (Addison-Wesley, 1990).

....can be resolved. We think that the use of large scale knowledge bases such as the WFB KB can help to close the gap between small canonical problems and the real world. Two notable efforts that have developed large scale knowledge bases are the botany knowledge base project [2] and the CYC project [3]. The WFB KB is also an experiment in knowledge reuse. We have reused definitions and axioms from the Ontolingua library [4] as well as the Upper Level ontology that has been developed within the High Performance Knowledge Base (HPKB) project of the Defense Advanced Research Projects Agency. ....

Lenat, D.B. and Guha, R.V. (1990) Building large knowledge-based systems: representation and inference in the Cyc project. Reading, Mass: Addison-Wesley Pub. Co.

....and on which the prototype has been tested. 5. RELATED RESEARCH Due to space limitations we discuss here only three approaches closely related to MIBIA. Carnot [4] supports the development of applications operating on heterogeneous data sources in an enterprise 1 . It uses the Cyc ontology [14], which provides knowledge from different subject areas, to make explicit the semantics of the available data. Logical integration of a data source can be performed independently from others by specifying a bidirectional translation between local structures and concepts from Cyc. Carnot allows ....

Lenat, D.; Guha, R.: Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project, Addison-Wesley, Reading, Mass., 1990

....be extracted from general purpose encyclopaedias and dictionaries, common sense physics, philosophical categorizations (such as Plato s, Aristotle s, Lull s, Kant s, Peirce s (and Sowa s 21 ) Hartmann s 35 , etc. see 29 ) top levels of various computational large KBs (Penman 27 , CYC 37 , Roget s thesaurus, etc. and even through introspection. A domain ontology can be extracted from special purpose encyclopaedias, dictionaries, nomenclatures, taxonomies, handbooks, scientific special languages (say, chemical formulas) specialized KBs, and from experts. 2.4 Some excerpts ....

....encyclopaedic type definitions, or the logical riddle between analytic and synthetic categories. Marconi 38 gives an account of the problem in terms of a plausibility metrics. Other accounts have been given by evoking contextual solutions (for instance, the contextual triggering of CYC 37 , and contextual logics 14, 54 . The interest of such approaches notwithstanding, one still lacks an ontological criterion. This is quite obvious, because the stopover is dependent on task, and tasks can be modelled only for specialized, very limited, and conventional protocols of ....

Lenat DB, Guha RV. Building Large Knowledgebased Systems: Representation and Inference in the CYC Project. Menlo Park, Addison-Wesley, 1990

....will at least appear plausible, but it invites the input of an animator at any stage to affect the final output. Future work includes more extensive automatic linguistic tagging and additional inferencing, relying further on WordNet or even on a database of common sense knowledge, such as Cyc [21]. In addition further work is needed on the notion of the gesture ontology, including some basic spatial configuration gesture elements. As it stands, hand gestures cannot be assembled out of smaller gestural parts, nor can they be shortened. When gesture descriptions are read from the knowledge ....

Lenat, D. B. and Guha, R. V., Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Reading, MA: Addison Wesley, 1990.

....by relations and functions in the Ontolingua language) 8. Next we will define a set of axioms (e.g. Actinomycetes are a cause of abnormal state of biological origin) to represent the constraints over the objects and properties in the ontology. This set of axioms constitutes a microtheory [8] and provides a declarative specification for the various tasks we want to model. 9. Eventually, we need to prove results about the properties of our microtheory in order to provide a characterization and justification for our approach; this enables us to understand the scope and limitations of ....

Lenat, D. and Guha, R.V., 1990. Building Large Knowledge Based Systems: Representation and Inference in the CYC Project, Addison Wesley Pub. Co., 1990.

.... language glosses; taxonomy, e.g. the SNOMED taxonomy [6] or the UMLS Metathesaurus [28] a collection of concepts with a partial order induced by inclusion; axiomatized taxonomy, e.g. the GALEN Core Model [11] a taxonomy with axioms; context (or ontology) library, e.g. the CYC encyclopaedia [22]: a set of axiomatized taxonomies with relations among them (inclusion of a context into another one, or use of a concept from a context in another one) 2.2 Kinds of ontology modules The following is a classification of ontology modules (formal contexts) according to generality (an elaboration ....

Lenat DB, Guha RV, Building Large Knowledge-based Systems: Representation and Inference in the CYC Project, Menlo Park, Addison-Wesley, 1990.

....element of the library is a module, which can be included into another one. Also, a concept from a module can be only used into another one. Ontology modules can be considered subdivisions of the namespace of a model. Modules can also be assigned a context semantics, e.g. in CYC microtheories [33]. When ontologies are specified at the most refined formal level i.e. as modules in a library a further classification is needed which is based on the generality of the concepts and relations that are defined within a module. The following typology is an elaboration of, among others, Guarino ....

Lenat DB, Guha RV, Building Large Knowledge-based Systems: Representation and Inference in the CYC Project, Menlo Park, Addison-Wesley, 1990.

....spite of this, most Knowledge Base (KB) development efforts start from scratch, and once the project is over, the KB content is thrown away. As a result, it is difficult to amortize the cost of encoding knowledge over multiple projects and the KBs remain brittle, that is, limited in size and scope (Lenat and Guha 1990). To address this limitation, several complementary approaches have been attempted. Standards for representing, exchanging, and accessing knowledge have been developed (Genesereth and Fikes 1992) Large repositories of knowledge have been constructed so that they could serve as the starting point ....

....several complementary approaches have been attempted. Standards for representing, exchanging, and accessing knowledge have been developed (Genesereth and Fikes 1992) Large repositories of knowledge have been constructed so that they could serve as the starting point for new KB development efforts (Lenat and Guha 1990), Knight and Luk August 1994) This project builds upon this earlier work in an effort to reduce the cost of developing KBs. The project had three focus areas: content development techniques, new content development, and knowledge server extensions. There was a strong emphasis on content ....

Lenat, D. B. and R. V. Guha (1990). "Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project." : 336.

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D. Lenat and R.V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project, AddisonWesley, Reading, Mass., 1990.

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Lenat, D. B., and Guha, R. V. 1990. Building Large KnowledgeBased Systems: Representation and Inference in the CYC Project. Reading, Massachusetts: Addison-Wesley.

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Lenat, D., and Guha, R. V. 1990. Building Large KnowledgeBased Systems: Representation and Inference in the Cyc Project. Addison-Wesley.

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Lenat D & Guha R V (1990) Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project. Reading, MA: Addison-Wesley.

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D. Lenat and R. V. Guha. Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley, 1990.

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Lenat, D. B., & Guha, R. V. 1990. Building Large Knowledge-Based Systems: Representation and Inference in the CYC project. Reading, MA: AddisonWesley.

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D. B. Lenat and R. V. Guha. Building Large KnowledgeBased Systems: Representation and Inference in the Cyc Project. Addison-Wesley, February 1990.

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Lenat, D. B. and Guha, R. V. (1989). Building large knowledgebased systems: Representation and inference in the Cyc project, Addison-Wesley, Reading, MA.

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D. Lenat and D. Guha. Building large knowledge-based systems: Representation and Inference in the Cyc Project. Addison-Wesley, Reading, MA, 1990.

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D. B. Lenat and R. V. Guha. Building Large KnowledgeBased Systems: Representation and Inference in the Cyc Project. Addison-Wesley, February 1990.

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Lenat, D.B. and R.V. Guha, Building Large KnowledgeBased Systems: Representation and Inference in the CYC Project. 1990: p. 336.

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D.B. Lenat and R.V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc project. Addison Wesley Publishing Company, 1994.

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D. Lenat and R. Guha. Building Large Knowledge Based Systems: Representation and Inference in the Cyc Project. Reading, Mass,Addison-Wesley, 1990.

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Lenat, D.B. and R.V. Guha, Building Large KnowledgeBased Systems: Representation and Inference in the CYC Project. 1990: p. 336.

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D. Lenat and R. Guha. Building Large Knowledge Based Systems: Representation and Inference in the Cyc Project. Reading, Mass,Addison-Wesley, 1990.

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D. B. Lenat and R. V. Guha. Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley, Reading, MA, 1990.

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D. Lenat and R. V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project, Addison-Wesley Publishing Company, Inc., Reading, MA, 1990.

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Doug Lenat and R. V. Guha. Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley, Reading, Massachusetts, 1990.

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D.B. Lenat and R.V. Guha. Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project. Addison-Wesley, Reading, Massachusetts, 1990.

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D. Lenat and R. Guha. Building Large Knowledge Based Systems: Representation and Inference in the Cyc Project. Reading, Mass,Addison-Wesley, 1990.

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D. Lenat and R. Guha, Building Large KnowledgeBased Systems: Representation and Inference in the CYC Project. Redwood City, CA.: Addison-Wesley Publishing Co., Inc., 1990.

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Lenat, D. & Guha, R. (1990). Building Large Knowledge Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley Publishing. Reading, MA.

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Lenat, D. and Guha, R., "Building Large Knowledge-based Systems: Representation and Inference in the CYC Project", Addison-Wesley (1990).

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D. Lenat and R. Guha, Building Large Knowledge -Based Systems: Representation and Inference

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R.V. Guha D. Lenat. Building Large Knowledge-based Systems: Representation and Inference in the CYC Project. Addison-Wesly, Reading Mass., 1990.

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Lenat, D. and Guha, R.V. Building Large Knowledge-based Systems: Representation and Inference in the CYC Project. Addison Wesley, 1990.

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Lenat, D. B. and Guha, R. V., Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Reading, MA: Addison Wesley, 1990.

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