Ontologies [1] are used in computer science for representing and sharing knowledge about the real world. Usually ontological structures are described in terms of classes(of things) and relationships(between things). This is rather similar to category-theoretic notions of objects and morphisms (see [2, 3] for information about the algebraic category theory). Since the category theory already brings us many benefits in other areasofcomputer science, it is desirable to find arrowtheoretic approaches in the area of knowledge representation. 1 Some authors proposed category-theoretic techniques helpful in different aspects of knowledge representation[5, 6]. Usually they operate with (co)limits that are convenient for merging and interoperating between existing models and metamodels. Our aim is to find a category-theoretic tools that would be useful for description of ontological models from the very beginning.

World Wide Web (WWW) is the most popular global information sharing and communication system consisting of three standards .i.e., Uniform Resource Identifier (URL), Hypertext Transfer Protocol (HTTP) and Hypertext Mark-up Language (HTML). Information is provided in text, image, audio and video formats over the web by using HTML which is considered to be unconventional in defining and formalizing the meaning of the context...

There is a growing need for efficient and scalable semantic web queries that handle inference. There is also a growing interest in representing uncertainty in semantic web knowledge bases. In this paper, we present a bit vector schema specifically designed for RDF (Resource Description Framework) datasets. We propose a system for materializing and storing inferred knowledge using this schema. We show experimental results that demonstrate that our solution drastically improves the performance of inference queries. We also propose a solution for materializing uncertain information and probabilities using multiple bit vectors and thresholds.

Conjunctive query answering is a key reasoning service for many ontology-based applications. In order to improve scalability, many Semantic Web query answering systems give up completeness (i.e., they do not guarantee to return all query answers). It may be useful or even critical to the designers and users of such systems to understand how much and what kind of information is (potentially) being lost. We present a method for generating test data that can be used to provide at least partial answers to these questions, a purpose for which existing benchmarks are not well suited. In addition to developing a general framework that formalises the problem, we describe practical data generation algorithms for some popular ontology languages, and present some very encouraging results from our preliminary evaluation.

In this abstract, we compare semantic search (in the RDF model) with keyword search (in the relational model), and illustrate how these two search paradigms are different. This comparison addresses the following questions: (1) What can semantic search achieve that keyword search can not (in terms of behavior)? (2) Why is it difficult to simulate semantic search, using keyword search on the relational data model? We use the term keyword search, when the search is performed on data stored in the relational data model, as in traditional relational databases, and an example of keyword search in databases is [Hri02]. We use the term semantic search, when the search is performed on data stored in the RDF data model. Note that when the data is modeled in RDF, it inherently contains explicit typed relations or semantics, and hence the use of the term “semantic search.” Let us begin with an example, to illustrate the differences between semantic search and keyword search.

The UMLS Metathesaurus was assembled by integrating its ancestors. We introduced an inconsistency definition for some 150 source vocabularies; it contains more than Metathesaurus concepts based on their hierarchical relations 2 million concepts (i.e., clusters of synonymous terms coming and compute all such inconsistent concepts. After that we from multiple source vocabularies identified by a Concept manually review some of the inconsistent concepts to determine Unique Identifier). The UMLS Metathesaurus contains the ones that have erroneous synonymy relations such also more than 36 million relations between these concepts, as wrong synonymy.

Answering queries posed over knowledge bases is a central problem in knowledge representation and database theory. In the database area, checking query containment is an important query optimization and schema integration technique. In knowledge representation it has been used for object classification, schema integration, service discovery, and more. In the presence of a knowledge base, the problem of query containment is strictly related to that of query answering; indeed, the two are reducible to each other; we focus on the latter, and our results immediately extend to the former.

In many data-centric semantic web applications, it is desirable to use OWL to encode the Integrity Constraints (IC) that must be satisfied by instance data. However, challenges arise due to the Open World Assumption (OWA) and the lack of a Unique Name Assumption (UNA) in OWL’s standard semantics. In particular, conditions that trigger constraint violations in systems using the ClosedWorld Assumption (CWA), will generate new inferences in standard OWL-based reasoning applications. In this paper, we present an alternative IC semantics for OWL that allows applications to work with the CWA and the weak UNA. Ontology modelers can choose which OWL axioms to be interpreted with our IC semantics. Thus application developers are able to combine open world reasoning with closed world constraint validation in a flexible way. We also show that IC validation can be reduced to query answering under certain conditions. Finally, we describe our prototype implementation based on the OWL reasoner Pellet.

We describe a generic framework for representing and reasoning with annotated Semantic Web data, formalise the annotated language, the corresponding deductive system, and address the query answering problem. We extend previous contributions on RDF annotations by providing a unified reasoning formalism and allowing the seamless combination of different annotation domains. We demonstrate the feasibility of our method by instantiating it on (i) temporal RDF; (ii) fuzzy RDF; (iii) and their combination. A prototype shows that implementing and combining new domains is easy and that RDF stores can easily be extended to our framework.

Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and provide a formalization of the ontology matching problem within the framework. The approach has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of a-priori confidence values. We show empirically that the approach is efficient and more accurate than existing matchers on an established ontology alignment benchmark dataset.