Efficient support for querying large-scale resource description framework (RDF) triples plays an important role in semantic web data management. This paper presents an efficient RDF query engine to evaluate SPARQL queries, where the inverted index structure is employed for indexing the RDF triples. A set of operators on the inverted index was developed for query optimization and evaluation. Then a main-tree-shaped optimization algorithm was developed that transforms a SPARQL query graph into the...

Ontology diagnosis, a well-known approach for handling inconsistencies in a description logic (DL) based ontology, computes a diagnosis of the ontology, i.e., a minimal subset of axioms in the ontology whose removal restores consistency. However, ontology diagnosis is computationally hard, especially computing a minimum cost diagnosis (MCD) which is a diagnosis such that the sum of the removal costs attached to its axioms is minimized. This paper addresses this problem by finding data tractable ...

With the rapid development of Web 2.0, more and more people are sharing their opinions about online products, so there is much product review data. However, it is difficult to compare products directly using ratings because many ratings are based on different scales or ratings are even missing. This paper addresses the following question: given textual reviews, how can we automatically determine the semantic orientations of reviewers and then rank different items? Due to the absence of ratings i...

This paper investigates the problem of ranking linked data from relational databases using a ranking framework. The core idea is to group relationships by their types, then rank the types, and finally rank the instances attached to each type. The ranking criteria for each step considers the mapping rules and heterogeneous graph structure of the data web. Tests based on a social network dataset show that the linked data ranking is effective and easier for people to understand. This approach benef...

Description logic programs (DLP) are an expressive but tractable subset of OWL. This paper analyzes the important under-researched problem of learning DLP from uncertain data. Current studies have rarely explored the plentiful uncertain data populating the semantic web. This algorithm handles uncertain data in an inductive logic programming framework by modifying the performance evaluation criteria. A pseudo-log-likelihood based measure is used to evaluate the performance of different literals u...

The building of data mashups is complicated and error-prone, because this process requires not only finding suitable APIs but also combining them in an appropriate way to get the desired result. This paper describes an ontology-driven mashup auto-completion approach for a data API network to facilitate this task. First, a microformats-based ontology was defined to describe the attributes and activities of the data APIs. A semantic Bayesian network (sBN) and a semantic graph template were used fo...

Name ambiguity is a critical problem in many applications, in particular in online bibliography systems, such as DBLP, ACM, and CiteSeerx. Despite the many studies, this problem is still not resolved and is becoming even more serious, especially with the increasing popularity of Web 2.0. This paper addresses the problem in the academic researcher social network ArnetMiner using a supervised method for exploiting all side information including co-author, organization, paper citation, title simila...

The efficiency of QoS-aware service composition is important since most service composition problems are known to be NP-hard. With the growing number of web services, service composition is like a decision problem on selecting services or/and execution plans to satisfy the users' end-to-end QoS requirements (e.g. response time, throughput). Composite services with the same functionality may have different execution plans, which may cause different end-to-end QoS. This paper presents a model comb...

Reasoning with inconsistent ontologies involves using an inconsistency reasoner to get meaningful answers from inconsistent ontologies. This paper introduces an improved inconsistency reasoner, which selects consistent subsets using minimal inconsistent sets and a resolution method, to improve the run-time performance of the reasoning processing. A minimal inconsistent set contains a minimal explanation for the inconsistency of a given ontology. Thus, it can replace the consistency checking oper...

This paper describes the problem of doing description logic (DL) reasoning with partially closed world. The issue was addressed by extending the syntax of DL SROIQ with an NBox, which specifies the predicates to close, extending the semantics with the idea of negation as failure, reducing the closed world reasoning to incremental reasoning on classical DL ontologies, and applying the syntactic approximation technology to improve the reasoning performance. Compared with the existing DBox approach...

This paper is an empirical study of unsupervised sentiment classification of Chinese reviews. The focus is on exploring the ways to improve the performance of the unsupervised sentiment classification based on limited existing sentiment resources in Chinese. On the one hand, all available Chinese sentiment lexicons — individual and combined — are evaluated under our proposed framework. On the other hand, the domain dependent sentiment noise words are identified and removed using unla...

Literature searches on the Web result in great volumes of query results. A model is presented here to refine the search process using user interests. User interests are analyzed to calculate semantic similarity among the interest terms to refine the query. Traditional general purpose similarity measures may not always fit a domain specific context. This paper presents a similarity method for medical literature searches based on the biomedical literature knowledge source “MEDLINE”, th...

Ontology evolution is the timely adaptation of ontologies to changing requirements, which is becoming more and more important as ontologies become widely used in different fields. This paper shows how to address the problem of evolving ontologies with less manual case-based reasoning using an automatic selection mechanism. An automatic ontology evolution strategy selection framework is presented that automates the evolution. A minimal change impact algorithm is also developed for the framework. ...

An element may have heterogeneous semantic interpretations in different ontologies. Therefore, understanding the real local meanings of elements is very useful for ontology operations such as querying and reasoning, which are the foundations for many applications including semantic searching, ontology matching, and linked data analysis. However, since different ontologies have different preferences to describe their elements, obtaining the semantic context of an element is an open problem. A sem...