Querying Natural Logic Knowledge Bases

Troels Andreasen

, Henrik Bulskov

, Per Anker Jensen

and Jørgen Fischer Nilsson

Computer Science, Roskilde University, Denmark

Management, Society and Communication, Copenhagen Business School, Denmark

Mathematics and Computer Science, Technical University of Denmark, Denmark

Keywords:

From Natural Language to Natural Logic, Formal Ontologies, Deductive Querying of Natural-logic Knowl-

edge Bases, Path ﬁnding in Knowledge Bases, Logical Knowledge bases in Bio-informatics and Medicine.

Abstract:

This paper describes the principles of a system applying natural logic as a knowledge base language. Natural

logics are regimented fragments of natural language employing high level inference rules. We advocate the

use of natural logic for knowledge bases dealing with querying of classes in ontologies and class-relationships

such as are common in life-science descriptions. The paper adopts a version of natural logic with recursive

restrictive clauses such as relative clauses and adnominal prepositional phrases. It includes passive as well as

active voice sentences. We outline a prototype for partial translation of natural language into natural logic,

featuring further querying and conceptual path ﬁnding in natural logic knowledge bases.

1 INTRODUCTION

We describe principles for a prototype system for nat-

ural logic knowledge bases with focus on the query

answering functionalities and the internal systems

representations for achieving these functionalities.

Natural logics are forms of logic which approach

natural language forms (van Benthem, 1986). Thus,

sentences stated in natural logic can be read and un-

derstood by application domain experts without back-

ground in logic and computer science. This is in

contrast to, say, description logic and logical clauses

as in DATALOG. The applied natural logic dialect,

called NATURALOG, possesses desirable decidabil-

ity and tractability properties, similar to description

logics. However, the deductive query functionalities

differ in that concept terms are themselves considered

ﬁrst class objects subject to query answering.

The achieved deductive query answering func-

tionalities are realized by devising a joint graph form

for the given natural logic sentences. The system re-

shapes natural logic sentences into atomic sentences

forming the graph in a process termed atomization.

This graph form affords an ontological view by way

of the inclusion relation between concepts (concept

nodes) (Arp et al., 2015). Since the concepts in the

natural logic may be complex as a reﬂection of recur-

sively composed noun phrases, the ontology is gener-

ative (Andreasen and Nilsson, 2004; Andreasen and

Nilsson, 2014; Andreasen et al., 2015). This means

that ever more specialized concepts can be accommo-

dated in addition to and by means of given primitive

concept terms.

The graph view, in addition to deductive querying

assisted by high level inference rules, supports path

ﬁnding as a mechanism to associate two stated query

terms. This is particularly relevant in the considered

bio-domain where causal pathways are in focus, cf.

(Andreasen et al., 2017b). In general, this associa-

tion is computed as a shortest path composed of a se-

quence of relations connecting the terms.

We have previously described the applied natural

logic in (Nilsson, 2015; Andreasen et al., 2015) and

more recently with various linguistic extensions (An-

dreasen et al., 2016; Andreasen et al., 2017a). In our

ongoing stepwise syntactic and semantic extensions

of NATURALOG, here we further extend the natural

logic with passive voice forms and adverbial restric-

tions, which are central in the considered life science

domains and corpora. This logical approach is in con-

trast to established and rather succesful approaches to

text mining based on direct references to phrases in

concrete text sources and advanced information ex-

traction techniques, cf. for example (Li et al., 2014;

Kaewphan et al., 2012; Miwa et al., 2013).

An approach to acquisition of ontology from pro-

cessing natural language is introduced in (de Azevedo

et al., 2014). They present a principle of automated

Andreasen T., Bulskov H., Jensen P. and Fischer Nilsson J.

Querying Natural Logic Knowledge Bases.

DOI: 10.5220/0006574502940301

In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KEOD 2017), pages 294-301

ISBN: 978-989-758-272-1

ontology building based on a natural language trans-

lator for expressive ontologies. Our approach differs

from (de Azevedo et al., 2014) in our use of a natural

logic with accompanying graph representation instead

of description logic. A version of natural logic is also

exploited in (MacCartney and Manning, 2009).

Obviously, given the present state of computa-

tional semantics, a natural logic cannot accommodate

the intricate syntactic and semantic forms found in

natural language, not even in the somewhat stereo-

typic scientiﬁc language corpora. Our strategy is to

make the system extract as much as possible in a com-

putational text analysis governed by the applied target

natural logic. This part is addressed in section 5.

2 KNOWLEDGE BASE NATURAL

LOGIC

A Natural logic knowledge base consists simply of a

set of natural logic afﬁrmative sentences. The natu-

ral logic sentences consist of two concept terms con-

nected by a relation

Cterm

R Cterm

Linguistically, this typically corresponds to a subject

term followed by a transitive verb and a linguistic ob-

ject term as in the sample natural logic proposition

betacell produce insulin

where morphologically correct forms in the formal

logical language are neglected in favour of simplify-

ing streamlining. This is an example of an atomic nat-

ural logic sentence. In such atomic sentences the con-

cept terms are plain common nouns. More generally,

the concept terms are noun phrases with a head noun

optionally attributed with restrictions such as prepo-

sitional phrases and relative clauses, cf. the example:

cell that produce insulin reside-in pancreas

where the subject term comprises the restrictive rela-

tive clause that produce insulin. We also consider ad-

verbial prepositional phrases so that the relation (tran-

sitive verb) generalizes to include also relations intro-

duced by prepositions.

Semantically, the natural logic sentences express

relationships between classes of individuals, includ-

ing subclasses formed by the various linguistic re-

strictive expressions.

2.1 Implicit Quantiﬁers

The considered natural logic sentences are logical

propositions in that there are implicit quantiﬁers (lin-

guistic determiners) as in

every Cterm

R some Cterm

strictly giving every betacell produce some insulin for

betacell produce insulin.

As such the sentences are predicate logical sen-

tences in disguise as discussed in (Nilsson, 2015; An-

dreasen et al., 2016). However, the underlying predi-

cate logical construals are not appealed to in the pro-

totype design, which uses inference rules at the natu-

ral logic level as brieﬂy described in section 5.2 and

covered in more detail in (Andreasen et al., 2015).

The natural logic sentence forms

every Cterm

R every Cterm

and

some Cterm

R some Cterm

are also supported. The latter form is needed when

considering passive forms of active sentences.

2.2 Copula Sentences

A common and important special case is the copula

sentence form

Cterm

isa Cterm

as in the atomic sentence insulin isa hormone, which

expresses that the denotation of the subject term

Cterm

is included in the denotation of the object term

Cterm

Notice that every Cterm

R some Cterm

corre-

sponds to the description logic terminological sen-

tence form Cterm

v ∃ R.Cterm

and that the copula

form corresponds to Cterm

v Cterm

, cf. (Baader,

2007). Thus, one may conceive of all description

logic sentences as copula sentences, whereas our nat-

ural logic sentences also use the actually appearing

transitive full verb forms. Obviously, our natural logic

is much closer to natural language description logic

and therefore a natural logic knowledge base can be

understood directly by domain experts. For further

discussion of this issue, we refer to (Nilsson, 2015;

Andreasen et al., 2017a). The natural logic forms

some-some, which is necessary for representing pas-

sive sentences, and every-every are not supported in

common forms of description logic.

3 NATURAL LOGIC GRAPHS

The natural logic knowledge base takes form of a set

of natural logic sentences represented as a joint graph

whose nodes uniquely represent concepts across the

sentences, and whose directed labeled edges are

domain-dependent relations cf. (Smith et al., 2005).

In this view, the copula sentences form an onto-

logical structure partially ordered by the inclusion re-

lation isa. The non-copula natural logic sentences

then contribute with supplementary directed edges be-

tween nodes in the ontology proper. As a main rule,

the sentences in the ontology are deﬁnitional, whereas

the given non-copula sentences are observational or

empirical or normative in nature.

As a hallmark of our approach, the natural logic

sentences are computationally reshaped (atomized)

into atomic natural logic sentences. This is accom-

plished by introduction of interior auxiliary concepts

formed by the system from the compound terms. For

instance, the term cell that produce insulin gives rise to

the atomic concept cell-that-produce-insulin, which is

deﬁned by the two systems-generated atomic natural

logic sentences

cell-that-produce-insulin isa cell

cell-that-produce-insulin produce insulin

cell-that-produce-insulin

insulin

produce

cell

Figure 1: Graph representation of the term cell that produce

insulin.

with a contribution to the knowledge graph as illus-

trated in ﬁgure 1. Formally and internally the con-

cept cell-that-produce-insulin is primitive, just like cell

and insulin. Both of the two systems-generated sen-

tences are deﬁnitional and therefore form part of the

ontology proper. Logically, they are made to ensure

that if something is a cell and is simultaneously some-

thing that produce insulin then it is a cell-that-produce-

insulin.

In the process of computationally building the on-

tology from compound terms in sentences additional

nodes may be necessary. For instance, establishment

of cell-that-produce-insulin calls for introduction and

deﬁnition of the concept cell-that-produce-hormone,

given that insulin is a hormone. This may trigger a

cascading effect since hor mone is a substance and so

forth in the inclusion structure.Moreover, a subsump-

tion inference rule ensures that the inclusion cell-

that-produce-insulin isa cell-that-produce-hormone is

recorded, cf. ﬁgure 2. See also (Andreasen et al.,

2015).

cell-that-

produce-hormone

cellhormone

produce

cell-that-

produce-insulin

insulin

produce

Figure 2: Concept inferred by subsumption shown with

dashed lines.

Restrictions in concept terms may be nested as in

organ that contain cell that produce hormone under-

stood as organ (that contain cell (that produce hor-

mone)) or aligned as in cell in pancreas [and] that pro-

duce hormone understood as cell (in pancreas) (that

produce hor mone). Evidently, these concepts give

rise to a number of nodes in the generative ontology.

3.1 Passive Sentences

Passive sentences are highly frequent in scientiﬁc

texts because the agent, which is the subject of the

corresponding active sentence, is often left unspec-

iﬁed. Our natural logic distinguishes two passive

forms: one in which the agent is absent as in insulin

is released [in body], and one in which the agent is

present as in insulin is-produced-by betacell, where is-

produced-by is understood as the inverse relation of

produce.

At ﬁrst sight, a passive sentence such as insulin is

produced by betacells may seem to be merely a syn-

tactic variant of the corresponding active voice sen-

tence, in casu betacells produce insulin. However,

from a strictly formal logic point of view from [ev-

ery] betacell produce [some] insulin follows only logi-

cally the weaker [some] insulin is-produced-by [some]

betacell (assuming a non-empty class of betacell by

the principle of existential import) and not [every] in-

sulin is-produced-by [some] betacell, as explained in

(Nilsson, 2015). When arcs representing active tran-

sitive verbs (every-some) are traversed in the oppo-

site direction the corresponding passive interpretation

(some-some) is obtained and vice versa.

3.2 The Structure of the Knowledge

Base

The entire knowledge-base graph may be conceived

of as consisting of two interwoven parts: A genera-

tive skeleton ontology and a propositional knowledge

base. The generative ontology consists of copula sen-

tences with atomic concepts such as

betacell isa cell

insulin isa hormone

hormone isa protein

augmented with all the applied and derived compound

concept terms as explained above in section 3. The

propositional knowledge base is contributed by the

the non-copula natural logic sentences. This part of

the graph is made up of relations between two con-

cept nodes in the generative ontology as in

betacell produce insulin

4 FROM TEXT TO NATURAL

LOGIC

The natural logic graph is built from fragments of

natural language in domain texts by elaborating the

structure in these based on a grammar deﬁning natu-

ral logic. Each domain text is added to the knowledge

base by processing the text sentence by sentence and

extracting relational triples corresponding to natural

logic propositions of the form Cterm R Cterm. These

propositions are then added to the knowledge graph

and thereby extend the knowledge base with the con-

tribution from the text sentence. The transformation

(and the mediating role of NATURALOG) can be il-

lustrated thus:

Sentence from the text

↓

NATURALOG proposition

↓

Edges (triples) in the knowledge graph

The language recognized for extraction of triples

from source texts is deﬁned by the following basic

natural logic grammar:

Prop ::= Cterm R Cterm

Cterm ::= { NOUN | CompNoun} {RelClauseterm |

Prepterm} *

RelClauseterm ::= [that|which|who] R Cterm

R ::= VERB | R

Pas

| R

Adv

Pas

::= be VERBppp by

Adv

::= be VERBppp R

Prep

::= PREPOSITION

Prepterm ::= R

Prep

Cterm

CompNoun ::= { NOUN }

NOUN

By way of example, the sentence cells that produce

insulin are located in the pancreatic gland is recog-

nized by the grammar as cell that produce insulin is-

located-in pancreatic gland, where is-located-in is the

relational form R

Adv

in the grammar. The atomization

(see section 3) introduces the two atomic argument

terms: cell-that-produce-insulin and pancreatic-gland

and extracts an edge corresponding to the main propo-

sition:

cell-that-produce-insulin is-located-in

pancreatic-gland

as well as edges to express the meaning of the argu-

ment terms:

cell-that-produce-insulin isa cell

cell-that-produce-insulin produce insulin

The resulting subgraph, corresponding to the contri-

bution to the knowledge base by the example sen-

tence, is shown in ﬁgure 3.

Notice that the copula edges are black and un-

labelled (thus, with isa being implicit) and that edges

corresponding to non-copula relations are drawn in

cell-that-produce-insulin

insulin

produce

cell pancreatic-gland

located_in

gland

Figure 3: Contribution to the graph by the sentence cells

that produce insulin are located in the pancreatic gland.

grey. Furthermore, to distinguish deﬁnitional and ob-

servational contributions from the sentence, the deﬁ-

nitional parts are indicated by joined edges – e.g. the

two outgoing edges with joined tails from the node

cell-that-produce-insulin in ﬁgure 3 identify the def-

initional part corresponding to the concept cell that

produce insulin.

As it also appears from ﬁgure 3, the compound

noun pancreatic gland is atomized into pancreatic-

gland and an inclusion edge indicating that this is a

specialization of the more general concept gland.

Thus, for a given input sentence, ﬁrst of all, a

proposition of the form Cterm R Cterm is recognized

and extracted as an observational R-arc (located in-

arc in ﬁgure 3) to be included in the graph. In addi-

tion, the contributions from each of the subject and

object term arguments (left and right Cterms) are ex-

tracted by atomization (decomposition): an atom cor-

responding to the compound is introduced (e.g. cell-

that-produce-insulin in ﬁgure 3) and arcs are added to

deﬁne the corresponding concept (e.g. the connec-

tions to cell and insulin in ﬁgure 3). Notice that these

additional argument contributions will always be def-

initional and thus be included in the generative part of

the ontology.

The grammar accepts passive sentences, so that

sentences like glucose production is inhibited by con-

centrations of insulin will be recognized as shown in

ﬁgure 4. The active paraphrase of this sentence, con-

centrations of insulin inhibits glucose production, will

glucose_production

concentration-of-insulin

be_inhibit_by

production

insulin

concentration

Figure 4: Graph representation of glucose production is in-

hibited by concentrations of insulin.

concentration-of-insulin

glucose_production

inhibit

insulin

concentration

production

Figure 5: Graph representation of concentrations of insulin

inhibit glucose production.

be recognized as well, and the extracted graph for this

is shown in ﬁgure 5. Observe that the extracted edges

are the same for the two sentences, except for the two

propositional relation edges, inhibit and be-inhibit-by.

5 A PROTOTYPE DESIGN

The approach described here involves two main chal-

lenges that relate to the introduced formalism for nat-

ural logic knowledge bases. Firstly, how to build a

knowledge base on top of an initial skeleton genera-

tive ontology by processing source texts and adding

extracted content from these. Secondly, how to pro-

vide a query mechanism that makes it possible to ex-

plore and reason with the content in the base, i.e. with

the knowledge extracted from the source text corpus.

Prototypes for knowledge base building as well as

for querying are under development and these will be

brieﬂy described below.

5.1 Building the Knowledge Base

As exempliﬁed above, the parsing of input sentences

provides contributions to the generative as well as the

propositional part of the ontology. However, to make

it possible to take advantage of valuable ressources

covering the domain of the given text corpus, our

approach introduces the notion of a skeleton ontol-

ogy. A generative skeleton ontology is basically a

collection of copula sentences and thus comprises

a vocabulary of what can be considered as atomic

concepts partially ordered in a taxonomic structure

by the isa relation. In the experiments performed

with the present prototype, we draw on excerpts from

SNOMED (Spackman et al., 1997) to build the skele-

ton ontology. Figure 6 shows an example of an initial

skeleton ontology based on a miniature excerpt from

SNOMED .

The initial skeleton ontology is a graph represent-

ing a natural logic knowledge base, and adding (or

loading) a text into the knowledge base simply means

to extend it with triples extracted from the given text.

Obviously, not in all cases will the extraction reveal

the full meaning of the sentence, but even partial

records of content may be valuable contributions to

the knowledge base.

Furthermore, there may be “knowledge gaps” be-

tween the skeleton ontology and propositions ex-

tracted from texts. To ﬁll these, we will rely on knowl-

edge added by domain experts. Such domain knowl-

edge could be added as taxonomic structures as in the

case of the skeleton ontology or as sentences express-

ing propositions as extracted from texts. As an ex-

ample, ﬁgure 7 shows the knowledge base after the

addition of a single piece of domain knowledge

Beta cells produce insulin

and the sentence

Glucose production by the liver is inhibited by

high concentrations of insulin in the blood

Triples are extracted by applying the principles

sketched in section 4 to every sentence in the input

text. More speciﬁcally, to extract triples from a sen-

tence, we devise a shallow analysis identifying con-

stituents in a ﬁrst preprocessing phrase and then link

these into a sentence structure in a subsequent parsing

phase, as described below.

5.1.1 Preprocessing

In the preprocessing phase the words in the sen-

tence are marked up by word-category and lemma-

tized. The sentence is tokenised into a list of lists,

where each word from the sentence is represented

by a list of possible canonical lemma and word cat-

egory (part of speech) combinations. Marked cate-

gories are thus not completely disambiguated. Fur-

thermore, the preprocessing applies a domain speciﬁc

vocabulary to identify multiword expressions in the

input sentence and replaces these by unique symbols.

Thus, a preprocessing of the sentence Beta cells pro-

duce insulin returns the following tagged and lem-

matized word list, where the word sequence ’Beta

cells’ is replaced by a symbol: {{beta cell/NOUN},

{produce/NOUN,produce/VERB}, {insulin/NOUN}}.

5.1.2 Parsing

In the second phase, the marked up sentence is parsed

using the natural logic grammar presented in section

4, and triples are extracted. The parsing is devised as

a top down processing where we try to cover as much

as possible of the considered sentence in a (partial)

“best ﬁt” process. As explained in section 4, the sen-

tence is recognized as a proposition centered around

insulin

hormone

enzyme blood

substance

SNOMED-concept

liver

body structure

glucose

sugar endocrine pancreas cell

Figure 6: Initial graph including a skeleton ontology based on a miniature excerpt from SNOMED .

the main verb and subject and object terms are atom-

ized leading to additional triples reﬂecting the content

in these.

The “best ﬁt” approach is basically a guiding prin-

ciple aiming for the largest possible coverage of the

input text. Thus, if an expression that covers the full

input sentence can be derived, it would be considered

the “best”, and if not, the aim is a partial coverage

where larger means “better”. Hence, the parser should

be able to recognize an input proposition if one such

exists for at least one combination of possible lem-

mata of the input words. Therefore, in addition to pro-

cessing the grammar (given above), the parser must

ensure that all combinations are tried before failing

the recognition of a proposition.

5.2 Query Answering

In order to enable exploration and reasoning with

the content of the graph, and thereby the knowledge

extracted from the source text corpus, we devise a

query mechanism comprising two types of queries:

Afﬁrmation Queries. The simplest form of queries

asks for an afﬁrmation (or rejection) of a sentence by

appealing to one of the monotonicity inference rules.

The monotonicity rules are inheritance and property

generalization (Andreasen et al., 2015) admitting, re-

spectively, specialization of the linguistic subject term

and generalization of the linguistic object term. For

instance

betacell produce hormone

follows by object generalization from the pair of

recorded sentences betacell produce insulin and in-

sulin isa hormone.

Query sentences may further contain variables in

noun phrase positions as in the scheme

X produce insulin

and in

betacell produce Y

The answer to queries using variables will be bind-

ings to the speciﬁed variables. So, in the former case

we will get Cterms corresponding to all kinds of cells

known to produce insulin “looking downwards” in the

ontology. Conversely, a query may ask for the prop-

erties of a concept as in

betacell isa Z

“looking upwards” in the ontology in order to retrieve

stated deﬁnitional properties of a concept.

In addition to the more traditional deductive

querying principle above, we put forward a new

principle of querying by abstraction over relations.

Connection Abstraction Queries. The so-called

“connection” or chaining abstraction retrieves short-

est relational paths between two stated concepts from

the ontology. The relational paths are computed and

delivered as explanatory answers to the query.

A connection abstraction is requested by letting a

relation term appear as a variable as R in

betacell R insulin

Formally this becomes generalized to graph path ﬁnd-

ing between stated concept terms by admitting that re-

lation variables R be instantiated to composed relation

terms. These terms represent (heuristically weighted)

shortest paths in the knowledge base graph as in a

hypothetical query liver R glucose, which calls for

traversal of a path to be given as answer to the in-

formal question “what is the connection between the

liver and glucose?”. One answer to this is exempliﬁed

in ﬁgure 8.

Chaining abstraction is particularly relevant in the

bio-domain due to the interest in biochemical causa-

tion and conversion paths.

glucose_production-by-liver

high_concentration-of-insulin-in-blood

be_inhibit_by

liver

glucose_production

insulin-in-blood

high_concentration

blood

insulin

substance

hormone

body-structure

production

glucose

produce

sugar

beta_cell

produce

cell

enzyme

SNOMED-concept

Figure 7: Graph after the addition of (1) Beta cell produce insulin, (2) Glucose production by the liver is inhibited by high

concentrations of insulin in the blood and (3) Glucose production produce glucose.

glucose_production glucose

produce

glucose_production-by-liver liver

Figure 8: Connection between liver and glucose.

6 SUMMARY AND CONCLUSION

We have described the key principles of a prototype

system intended for deductive querying and pathway

ﬁnding in knowledge bases. The knowledge base lan-

guage is a form of natural logic. The prototype un-

der development performs a partial translation of nat-

ural language input texts into the natural logic. This

translation is limited by the semantic coverage of the

natural logic. The natural logic sentences are atom-

ized into an internal graph representation where the

nodes represent complex as well as atomic concepts.

This graph representation facilitates pathﬁnding be-

tween concepts. Assessment of the viability of this

natural logic approach calls for further development

of and experimentation with the prototype.

REFERENCES

Andreasen, T., Bulskov, H., Jensen, P. A., and Nilsson,

J. F. (2017a). Partiality, Underspeciﬁcation, and Nat-

ural Language Processing, chapter A Natural Logic

for Natural-Language Knowledge Bases. Cambridge

Scholars.

Andreasen, T., Bulskov, H., Jensen, P. A., and Nilsson,

J. F. (2017b). Pathway Computation in Models De-

rived from Bio-Science Text Sources, pages 424–434.

Springer International Publishing, Cham.

Andreasen, T., Bulskov, H., Nilsson, J. F., and Jensen, P. A.

(2015). A system for conceptual pathway ﬁnding and

deductive querying. In Flexible Query Answering Sys-

tems 2015, pages 461–472. Springer.

Andreasen, T., Bulskov, H., Nilsson, J. F., and Jensen,

P. A. (2016). On the relationship between a compu-

tational natural logic and natural language. In van den

Herik; Joaquim Filipe, J., editor, the 8th International

Conference on Agents and Artiﬁcial Intelligence, vol-

ume 1.

Andreasen, T. and Nilsson, J. F. (2004). Grammatical spec-

iﬁcation of domain ontologies. Data Knowl. Eng.,

48(2):221–230.

Andreasen, T. and Nilsson, J. F. (2014). A case for em-

bedded natural logic for ontological knowledge bases.

In 6th International Conference on Knowledge Engi-

neering and Ontology Development.

Arp, R., Smith, B., and Spear, A. D. (2015). Building On-

tologies with Basic Formal Ontology. The MIT Press.

Baader, F. (2007). The description logic handbook: theory,

implementation, and applications. Cambridge Univer-

sity Press, Cambridge, 2nd ed edition.

de Azevedo, R. R., Freitas, F., Rocha, R., de Menezes, J.

A. A., and Pereira, L. F. A. (2014). Generating de-

scription logic ALC from text in natural language. In

Foundations of Intelligent Systems, pages 305–314.

Springer.

Kaewphan, S., Kreula, S., Van Landeghem, S., Van de Peer,

Y., Jones, P. R., and Ginter, F. (2012). Integrating

large-scale text mining and co-expression networks:

Targeting nadp(h) metabolism in e. coli with event

extraction. In Proceedings of the Third Workshop

on Building and Evaluating Resources for Biomedical

Text Mining (BioTxtM 2012), pages 8–15.

Li, C., Liakata, M., and Rebholz-Schuhmann, D. (2014).

Biological network extraction from scientiﬁc litera-

ture: state of the art and challenges. Brieﬁngs in Bioin-

formatics, 15(5):856–877.

MacCartney, B. and Manning, C. D. (2009). An extended

model of natural logic. In Proceedings of the eighth

international conference on computational semantics,

pages 140–156. Association for Computational Lin-

guistics.

Miwa, M., Ohta, T., Rak, R., Rowley, A., Kell, D. B.,

Pyysalo, S., and Ananiadou, S. (2013). A method for

integrating and ranking the evidence for biochemical

pathways by mining reactions from text. Bioinformat-

ics, 29(13):44–52.

Nilsson, J. F. (2015). In pursuit of natural logics for

ontology-structured knowledge bases. In The Sev-

enth International Conference on Advanced Cognitive

Technologies and Applications.

Smith, B., Ceusters, W., Klagges, B., K

ohler, J., Kuma, A.,

Lomax, J., Mungall, C., Neuhaus, F., Rector, A., and

Rosse, C. (2005). Relations in biomedical ontologies.

Genome Biology, 6(5):R46.

Spackman, K. A., D, P., Campbell, K. E., D, P., Ct, R. A.,

and (hon, D. S. (1997). Snomed rt: A reference termi-

nology for health care. In J. of the American Medical

Informatics Association, pages 640–644.

van Benthem, J. (1986). Essays in Logical Semantics, Vol-

ume 29 of Studies in Linguistics and Philosophy. D.

Reidel, Dordrecht, Holland.