Vanessa Lopez - Academia.edu (original) (raw)

Papers by Vanessa Lopez

Robust mechanisms for ontology selection are crucial for the evolving Semantic Web characterized ... more Robust mechanisms for ontology selection are crucial for the evolving Semantic Web characterized by rapidly increasing numbers of online ontologies and by applications that automatically use the associated metadata. However, existing selection techniques have primarily been designed in the context of human mediated tasks and fall short of supporting automatic knowledge reuse. We address this gap by proposing a selection algorithm that takes into account 1) the needs of two applications that explore large scale, distributed markup and 2) some properties of online ontology repositories. We conclude that the ambitious context of automatic knowledge reuse imposes several challenging requirements on selection.

The semantic web vision is one in which rich, ontology-based semantic markup is widely available,... more The semantic web vision is one in which rich, ontology-based semantic markup is widely available, both to enable sophisticated interoperability among agents and to support human web users in locating and making sense of information. The availability of semantic markup on the web also opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from one or more knowledge bases (KBs), which instantiate the input ontology with domain-specific information. AquaLog makes use of the GATE NLP platform, string metrics algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target knowledge base. Finally, although AquaLog has primarily been designed for use with semantic web languages, it makes use of a generic plug-in mechanism, which means it can be easily interfaced to different ontology servers and knowledge representation platforms.

IEEE Expert / IEEE Intelligent Systems, 2008

As semantic markup becomes ubiquitous, it will become important to be able to ask queries and obt... more As semantic markup becomes ubiquitous, it will become important to be able to ask queries and obtain answers, using natural language (NL) expressions, rather than the keyword-based retrieval mechanisms used by the current search engines. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from the available semantic markup. We say that AquaLog is portable, because the configuration time required to customize the system for a particular ontology is negligible. AquaLog combines several powerful techniques in a novel way to make sense of NL queries and to map them to semantic markup. Moreover it also includes a learning component, which ensures that the performance of the system improves over time, in response to the particular community jargon used by the end users. In this paper we describe the current version of the system, in particular discussing its portability, its reasoning capabilities, and its learning mechanism.

Journal of Web Semantics, 2007

International Journal of Metadata, Semantics and Ontologies, 2010

Because metadata that underlies semantic web applications is gathered from distributed and hetero... more Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a verification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation comparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata.

Knowledge Engineering Review, 2007

The goal of semantic search is to improve on traditional search methods by exploiting the semanti... more The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web.

In this paper we propose algorithms for combining and ranking answers from distributed heterogene... more In this paper we propose algorithms for combining and ranking answers from distributed heterogeneous data sources in the context of a multi-ontology Question Answering task. Our proposal includes a merging algorithm that aggregates, combines and filters ontology-based search results and three different ranking algorithms that sort the final answers according to different criteria such as popularity, confidence and semantic interpretation of results. An experimental evaluation on a large scale corpus indicates improvements in the quality of the search results with respect to a scenario where the merging and ranking algorithms were not applied. These collective methods for merging and ranking allow to answer questions that are distributed across ontologies, while at the same time, they can filter irrelevant answers, fuse similar answers together, and elicit the most accurate answer(s) to a question.

Robust mechanisms for ontology selection are crucial for the evolving Semantic Web characterized ... more Robust mechanisms for ontology selection are crucial for the evolving Semantic Web characterized by rapidly increasing numbers of online ontologies and by applications that automatically use the associated metadata. However, existing selection techniques have primarily been designed in the context of human mediated tasks and fall short of supporting automatic knowledge reuse. We address this gap by proposing a selection algorithm that takes into account 1) the needs of two applications that explore large scale, distributed markup and 2) some properties of online ontology repositories. We conclude that the ambitious context of automatic knowledge reuse imposes several challenging requirements on selection.

The semantic web vision is one in which rich, ontology-based semantic markup is widely available,... more The semantic web vision is one in which rich, ontology-based semantic markup is widely available, both to enable sophisticated interoperability among agents and to support human web users in locating and making sense of information. The availability of semantic markup on the web also opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from one or more knowledge bases (KBs), which instantiate the input ontology with domain-specific information. AquaLog makes use of the GATE NLP platform, string metrics algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target knowledge base. Finally, although AquaLog has primarily been designed for use with semantic web languages, it makes use of a generic plug-in mechanism, which means it can be easily interfaced to different ontology servers and knowledge representation platforms.

IEEE Expert / IEEE Intelligent Systems, 2008

As semantic markup becomes ubiquitous, it will become important to be able to ask queries and obt... more As semantic markup becomes ubiquitous, it will become important to be able to ask queries and obtain answers, using natural language (NL) expressions, rather than the keyword-based retrieval mechanisms used by the current search engines. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from the available semantic markup. We say that AquaLog is portable, because the configuration time required to customize the system for a particular ontology is negligible. AquaLog combines several powerful techniques in a novel way to make sense of NL queries and to map them to semantic markup. Moreover it also includes a learning component, which ensures that the performance of the system improves over time, in response to the particular community jargon used by the end users. In this paper we describe the current version of the system, in particular discussing its portability, its reasoning capabilities, and its learning mechanism.

Journal of Web Semantics, 2007

International Journal of Metadata, Semantics and Ontologies, 2010

Because metadata that underlies semantic web applications is gathered from distributed and hetero... more Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a verification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation comparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata.

Knowledge Engineering Review, 2007

The goal of semantic search is to improve on traditional search methods by exploiting the semanti... more The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web.

In this paper we propose algorithms for combining and ranking answers from distributed heterogene... more In this paper we propose algorithms for combining and ranking answers from distributed heterogeneous data sources in the context of a multi-ontology Question Answering task. Our proposal includes a merging algorithm that aggregates, combines and filters ontology-based search results and three different ranking algorithms that sort the final answers according to different criteria such as popularity, confidence and semantic interpretation of results. An experimental evaluation on a large scale corpus indicates improvements in the quality of the search results with respect to a scenario where the merging and ranking algorithms were not applied. These collective methods for merging and ranking allow to answer questions that are distributed across ontologies, while at the same time, they can filter irrelevant answers, fuse similar answers together, and elicit the most accurate answer(s) to a question.