=Paper=
{{Paper
|id=None
|storemode=property
|title=Querying Biomedical Ontologies in Natural Language using Answer Set Programming
|pdfUrl=https://ceur-ws.org/Vol-698/poster6.pdf
|volume=Vol-698
|dblpUrl=https://dblp.org/rec/conf/swat4ls/ErdoganOEE10
}}
==Querying Biomedical Ontologies in Natural Language using Answer Set Programming==
https://ceur-ws.org/Vol-698/poster6.pdf
Querying Biomedical Ontologies in Natural Language
using Answer Set Programming
Halit Erdogan1 , Umut Oztok1 , Yelda Erdem2 , and Esra Erdem1
1
Faculty of Engineering and Natural Sciences, Sabancı University, İstanbul, Turkey
2
Research and Development Department, Sanovel Pharmaceutical Inc., İstanbul, Turkey
Recent advances in health and life sciences have led to generation of a large amount
of data. To facilitate access to its desired parts, such a big mass of data has been repre-
sented in structured forms, like biomedical ontologies. On the other hand, representing
ontologies in a formal language, constructing them independently from each other and
storing them at different locations have brought about many challenges for answering
queries about the knowledge represented in these ontologies. One of the challenges for
the users is to be able represent a complex query in a natural language, and get its
answers in an understandable form: Currently, such queries are answered by software
systems in a formal language, however, the majority of the users lack the necessary
knowledge of a formal query language to represent a query; moreover, none of these
systems can provide informative explanations about the answers. Another challenge is
to be able to answer complex queries that require appropriate integration of relevant
knowledge stored in different places and in various forms.
In this work, we address the first challenge by developing an intelligent user in-
terface that allows users to enter biomedical queries in a natural language, and that
presents the answers (possibly with explanations if requested) in a natural language.
We address the second challenge by developing a rule layer over biomedical ontologies
and databases, and use automated reasoners to answer queries considering relevant parts
of the rule layer. The main contributions of our work can be summarized as follows:
– We introduce a controlled natural language, a subset of natural language with a re-
stricted grammar and vocabulary, specifically for biomedical queries towards drug
discovery; we call this controlled natural language as B IO Q UERY CNL [4]. For
instance, in this language, we can pose the following query:
“What are the genes that are targeted by the drug Epinephrine and that
interact with the gene DLG4?”
– We present an algorithm that converts a biomedical query in B IO Q UERY CNL into
a program in answer set programming (ASP) — a formal framework to automate
reasoning about knowledge [8] — making use of the parsing engine APE [5]. Fig-
ure 1 shows the overall idea behind this algorithm. For instance, according to this
algorithm, the query above is translated into the following ASP program:
what_be_genes(GN1) :-
gene_gene(GN1,"DLG4"),
drug_gene("Epinephrine",GN1).
where gene gene and drug gene are defined in a “rule layer”.
2 Erdogan, Oztok, Erdem and Erdem
Fig. 1. Transforming a query in B IO Q UERY CNL into an ASP program.
– Once we transform the biomedical query into an ASP program and extract the rel-
evant part of the rule layer (also an ASP program), we can compute its answers
(if exists) using a state-of-the-art ASP system, such as CLASP [6], DLV [3,7] or
DLVHEX [2], as described in [1]. Figure 2 shows the overall idea behind this algo-
Querying Biomedical Ontologies using ASP 3
Fig. 2. Extracting and integrating knowledge from ontologies or databases that is relevant to a
given query, and finding an answer to the query using an ASP system.
rithm. For instance, using CLASP, we compute the following answer to the query
above: “ADRB1”.
– We construct an algorithm to provide minimal explanations to the answers. For
instance, for “ADRB1” our algorithm provides the following minimal explanation:
the drug “Epinephrine” targets the gene “ADRB1” according to CTD and
the gene “ADRB1” interacts with the gene “DLG4” according to B IO G RID.
The applicability of our methods is illustrated with some complex queries over
P HARM GKB, D RUG BANK, B IO G RID, S IDER and CTD, using the ASP systems CLASP
(with GRINGO), DLV and DLVHEX.
Acknowledgments. This work has been supported by TUBITAK Grant 108E229.
References
1. Bodenreider, O., Coban, Z.H., Doganay, M.C., Erdem, E.: A preliminary report on answering
complex queries related to drug discovery using answer set programming. In: Proc. of the 3rd
4 Erdogan, Oztok, Erdem and Erdem
International Workshop on Applications of Logic Programming to the Semantic Web and Web
Services (2008)
2. Eiter, T., G.Ianni, R.Schindlauer, H.Tompits: Effective integration of declarative rules with
external evaluations for Semantic-Web reasoning. In: Proc. of ESWC (2006)
3. Eiter, T., Leone, N., Mateis, C., Pfeifer, G., Scarcello, F.: A deductive system for non-
monotonic reasoning. In: Proc. of LPNMR. pp. 364–375 (1997)
4. Erdem, E., Yeniterzi, R.: Transforming controlled natural language biomedical queries into
answer set programs. In: Proc. of the Workshop on BioNLP. pp. 117–124 (2009)
5. Fuchs, N.E.: Attempto controlled english. In: Proc. of WLP. pp. 211–218 (2000)
6. Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: T.: Conflict-driven answer set solving.
In: Proc. of IJCAI. pp. 386–392 (2007)
7. Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., Scarcello, F.: The dlv system
for knowledge representation and reasoning. ACM Trans. Comput. Log. 7(3), 499–562 (2006)
8. Lifschitz, V.: What is answer set programming? In: Proc. of AAAI (2008)